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USENIX, The Advanced Computing Systems Association

16th USENIX Security Symposium

Pp. 183–198 of the Proceedings

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Integrity Checking in Cryptographic File Systems
with Constant Trusted Storage

Alina Oprea1 - Michael K. Reiter2

Abstract:

In this paper we propose two new constructions for protecting the integrity of files in cryptographic file systems. Our constructions are designed to exploit two characteristics of many file-system workloads, namely low entropy of file contents and high sequentiality of file block writes. At the same time, our approaches maintain the best features of the most commonly used algorithm today (Merkle trees), including defense against replay of stale (previously overwritten) blocks and a small, constant amount of trusted storage per file. Via implementations in the EncFS cryptographic file system, we evaluate the performance and storage requirements of our new constructions compared to those of Merkle trees. We conclude with guidelines for choosing the best integrity algorithm depending on typical application workload.


1 Introduction

The growth of outsourced storage in the form of storage service providers underlines the importance of developing efficient security mechanisms to protect files stored remotely. Cryptographic file systems (e.g., [10,6,25,13,17,23,20]) provide means to protect file secrecy (i.e., prevent leakage of file contents) and integrity (i.e., detect the unauthorized modification of file contents) against the compromise of the file store and attacks on the network while blocks are in transit to/from the file store. Several engineering goals have emerged to guide the design of efficient cryptographic file systems. First, cryptographic protections should be applied at the granularity of individual blocks as opposed to entire files, since the latter requires the entire file to be retrieved to verify its integrity, for example. Second, the application of cryptographic protections to a block should not increase the block size, so as to be transparent to the underlying block store. (Cryptographic protections might increase the number of blocks, however.) Third, the trusted storage required by clients (e.g., for encryption keys and integrity verification information) should be kept to a minimum.

In this paper we propose and evaluate two new algorithms for protecting file integrity in cryptographic file systems. Our algorithms meet these design goals, and in particular implement integrity using only a small constant amount of trusted storage per file. (Of course, as with any integrity-protection scheme, this trusted information for many files could itself be written to a file in the cryptographic file system, thereby reducing the trusted storage costs for many files to that of only one. The need for trusted information cannot be entirely eliminated, however.) In addition, our algorithms exploit two properties of many file-system workloads to achieve efficiencies over prior proposals. First, typical file contents in many file-system workloads have low empirical entropy; such is the case with text files, for example. Our first algorithm builds on our prior proposal that exploits this property [26] and uses tweakable ciphers [21,15] for encrypting file block contents; this prior proposal, however, did not achieve constant trusted storage per file. Our second algorithm reduces the amount of additional storage needed for integrity by using the fact that low-entropy block contents can be compressed enough to embed a message-authentication code inside the block. The second property that we exploit in our algorithms to reduce the additional storage needed for integrity is that blocks of the same file are often written sequentially, a characteristic that, to our knowledge, has not been previously utilized.

By designing integrity mechanisms that exploit these properties, we demonstrate more efficient integrity protections in cryptographic file systems than have previously been possible for many workloads. The measures of efficiency that we consider include the amount of untrusted storage required by the integrity mechanism (over and above that required for file blocks); the integrity bandwidth, i.e., the amount of this information that must be accessed (updated or read) when accessing a single file block, averaged over all blocks in a file, all blocks in all files, or all accesses in a trace (depending on context); and the file write and read performance costs.

The standard against which we compare our algorithms is the Merkle tree [24], which to date is the overwhelmingly most popular method of integrity protection for a file. Merkle trees can be implemented in cryptographic file systems so as to meet the requirements outlined above, in particular requiring trusted storage per file of only one output of a cryptographic hash function (e.g., 20 bytes for SHA-1 [30]). They additionally offer an integrity bandwidth per file that is logarithmic in the number of file blocks. However, Merkle trees are oblivious to file block contents and access characteristics, and we show that by exploiting these, we can generate far more efficient integrity mechanisms for some workloads.

We have implemented our integrity constructions and Merkle trees in EncFS [14], an open-source user-level file system that transparently provides file block encryption on top of FUSE [12]. We provide an evaluation of the three approaches with respect to our measures of interest, demonstrating how file contents, as well as file access patterns, have a great influence on the performance of the new integrity algorithms. Our experiments demonstrate that there is not a clear winner among the three constructions for all workloads, in that different integrity constructions are best suited to particular workloads. We thus conclude that a cryptographic file system should implement all three schemes and give higher-level applications an option to choose the appropriate integrity mechanism.


2 Random Access Integrity Model

We consider the model of a cryptographic file system that provides random access to files. Encrypted data is stored on untrusted storage servers and there is a mechanism for distributing the cryptographic keys to authorized parties. A small (on the order of several hundred bytes), fixed-size per file, trusted storage is available for authentication data.

We assume that the storage servers are actively controlled by an adversary. The adversary can adaptively alter the data stored on the storage servers or perform any other attack on the stored data, but it cannot modify or observe the trusted storage. A particularly interesting attack that the adversary can mount is a replay attack, in which stale data is returned to read requests of clients. Using the trusted storage to keep some constant-size information per file, and keeping more information per file on untrusted storage, our goal is to design and evaluate integrity algorithms that allow the update and verification of individual blocks in files and that detect data modification and replay attacks.

In our framework, a file $ \file$ is divided into n fixed-size blocks $ B_1 B_2 \dots B_n$ (the last block $ B_n$ might be shorter than the first n-1 blocks), each encrypted individually with the encryption key of the file and stored on the untrusted storage servers (n differs per file). The constant-size, trusted storage for file $ \file$ is denoted TSF. Additional storage for file $ \file$, which can reside in untrusted storage, is denoted USF; of course, USF can be written to the untrusted storage server.

The storage interface provides two basic operations to the clients: $ \file.\writeblock(i,\encb)$ stores content C at block index i in file $ \file$ and $ \encb \leftarrow \file.\readblock(i)$ reads (encrypted) content from block index i in file $ \file$. An integrity algorithm for an encrypted file system consists of five operations. In the initialization algorithm Init for file $ \file$, the encryption key for the file is generated. In an update operation $ \update(i,\bc)$ for file $ \file$, an authorized client updates the i-th block in the file with the encryption of block content B and updates the integrity information for the i-th block stored in TSF and USF. In the check operation $ \checka(i,\encb)$ for file $ \file$, an authorized client first decrypts C and then checks that the decrypted block content is authentic, using the additional storage TSF and USF for file $ \file$. The check operation returns the decrypted block if it concludes that the block content is authentic and $ \bot$ otherwise. A client can additionally perform an append operation $ \append(\bc)$ for file $ \file$, in which a new block that contains the encryption of B is appended to the encrypted file, and a $ \delete$ operation that deletes the last block in a file and updates the integrity information for the file.

Using the algorithms we have defined for an integrity scheme for an encrypted file, a client can read or write at any byte offset in the file. For example, to write to a byte offset that is not at a block boundary, the client first reads the block to which the byte offset belongs, decrypts it and checks its integrity using algorithm Check. Then, the client constructs the new data block by replacing the appropriate bytes in the decrypted block, and calls Update to encrypt the new block and compute its integrity information.

In designing an integrity algorithm for a cryptographic file system, we consider the following metrics. First is the size of the untrusted storage USF; we will always enforce that the trusted storage TSF is of constant size, independent of the number of blocks. Second is the integrity bandwidth for updating and checking individual file blocks, defined as the number of bytes from USF (updated or read) when accessing a block of file $ \file$, averaged over either: all blocks in $ \file$ when we speak of a per-file integrity bandwidth; all blocks in all files when we speak of the integrity bandwidth of the file system; or all blocks accessed in a particular trace when we speak of one trace. Third is the performance cost of writing and reading files.

3 Preliminaries

3.1 Merkle Trees

Merkle trees [24] are used to authenticate n data items with constant-size trusted storage. A Merkle tree for data items $ M_1,
\dots, M_n$, denoted $ \mt(M_1,\dots,M_n)$, is a binary tree that has $ M_1,
\dots, M_n$ as leaves. An interior node of the tree with children $ C_L$ and $ C_R$ is the hash of the concatenation of its children (i.e., $ h(C_L \vert\vert C_R)$, for $ h:\{0,1\}^* \rightarrow
\{0,1\}^s$ a second preimage resistant hash function [29] that outputs strings of length s bits). If the root of the tree is stored in trusted storage, then all the leaves of the tree can be authenticated by reading from the tree a number of hashes logarithmic in n.

We define the Merkle tree for a file $ \file$ with n blocks $ B_1,\dots,B_n$ to be the binary tree $ \mtfile=\mt(h(1 \vert\vert B_1),$ $ \dots, h(n \vert\vert B_n))$. A Merkle tree with a given set of leaves can be constructed in multiple ways. We choose to append a new block in the tree as a right-most child, so that the tree has the property that all the left subtrees are complete. We define several algorithms for a Merkle tree , for which we omit the implementation details, due to space limitations.

- In the $ \updatetree(\rootparam,i,hval)$ algorithm for tree T, the hash stored at the i-th leaf of T counting from left to right) is updated to $ hval$. This triggers an update of all the hashes stored on the path from the i-th leaf to the root of the tree. It is necessary to first check that all the siblings of the nodes on the path from the updated leaf to the root of the tree are authentic. Finally, the updated root of the tree is output in R.

- The $ \checktree(\rootparam,i,hval)$ algorithm for tree T checks that the hash stored at the i-th leaf matches $ hval$. All the hashes stored at the nodes on the path from the i-th leaf to the root are computed and the root of T is checked finally to match the value stored in R.

- Algorithm $ \appendtree(\rootparam,hval)$ for tree T a new leaf u that stores the hash value $ hval$ to the tree, updates the path from this new leaf to the root of the tree and outputs the new root of the tree in R.

- The $ \deletetree(\rootparam)$ algorithm for tree T the last leaf from the tree, updates the remaining path to the root of the tree and outputs the new root of the tree in R.

3.2 Encryption Schemes and Tweakable Ciphers

An encryption scheme consists of a key generation algorithm Gen thatoutputs an encryption key, an encryption algorithm $ \enc_k(M)$ that outputs the encryption of a message $ M$ with secret key $ k$ and a decryption algorithm $ \dec_k(\encb)$ that outputs the decryption of a ciphertext C with secret key $ k$. A widely used secure encryption scheme is AES [2] in CBC mode [8].

A tweakable cipher [21,15] is, informally, a length-preserving encryption method that uses a tweak in both the encryption and decryption algorithms for variability. A tweakable encryption of a message $ M$ with tweak t and secret key $ k$ is denoted $ \enc_k^t(M)$ and, similarly, the decryption of ciphertext C with tweak t and secret key $ k$ is denoted $ \dec_k^t(\encb)$. The tweak is a public parameter, and the security of the tweakable cipher is based only on the secrecy of the encryption key. Tweakable ciphers can be used to encrypt fixed-size blocks written to disk in a file system. Suitable values of the tweak for this case are, for example, block addresses or block indices in the file. There is a distinction between narrow-block tweakable ciphers that operate on block lengths of 128 bits (as regular block ciphers) and wide-block tweakable ciphers that operate on arbitrarily large blocks (e.g., 512 bytes or 4KB). In this paper we use the term tweakable ciphers to refer to wide-block tweakable ciphers as defined by Halevi and Rogaway [15].

The security of tweakable ciphers implies an interesting property, called non-malleability [15], that guarantees that if only a single bit is changed in a valid ciphertext, then its decryption is indistinguishable from a random plaintext. Tweakable cipher constructions include CMC [15] and EME [16].

3.3 Efficient Block Integrity Using Randomness of Block Contents

Oprea et al. [26] provide an efficient integrity construction in a block-level storage system. This integrity construction is based on the experimental observation that contents of blocks written to disk usually are efficiently distinguishable from random blocks, i.e., blocks uniformly chosen at random from the set of all blocks of a fixed length. Assuming that data blocks are encrypted with a tweakable cipher, the integrity of the blocks that are efficiently distinguishable from random blocks can be checked by performing a randomness test on the block contents. The non-malleability property of tweakable ciphers implies that if block contents after decryption are distinguishable from random, then it is very likely that the contents are authentic. This idea permits a reduction in the trusted storage needed for checking block integrity: a hash is stored only for those (usually few) blocks that are indistinguishable from random blocks (or, in short, random-looking blocks).

An example of a statistical test IsRand [26] that can be used to distinguish block contents from random blocks evaluates the entropy of a block and considers random those blocks that have an entropy higher than a threshold chosen experimentally. For a block $ B$, $ \trand(B)$ returns 1 with high probability if $ B$ is a uniformly random block in the block space and 0, otherwise. Oprea et al. [26] provide an upper bound on the false negative rate of the randomness test that is used in the security analysis of the scheme.

We use the ideas from Oprea et al. [26] as a starting point for our first algorithm for implementing file integrity in cryptographic file systems. The main challenge to construct integrity algorithms in our model is to efficiently reduce the amount of trusted storage per file to a constant value. Our second algorithm also exploits the redundancy in file contents to reduce the additional space for integrity, but in a different way, by embedding a message authentication code (MAC) in file blocks that can be compressed enough. Both of these schemes build from a novel technique that is described in Section 4.1 for efficiently tracking the number of writes to file blocks.

4 Write Counters for File Blocks

All the integrity constructions for encrypted storage described in the next section use write counters for the blocks in a file. A write counter for a block denotes the total number of writes done to that block index. Counters are used to reduce the additional storage space taken by encrypting with a block cipher in CBC mode, as described in Section 4.2. Counters are also a means of distinguishing different writes performed to the same block address and as such, can be used to prevent against replay attacks.

We define several operations for the write counters of the blocks in a file $ \file$. The $ \updatectr(i)$ algorithm either initializes the value of the counter for the i-th block in file $ \file$ with 1, or it increments the counter for the i-th block if it has already been initialized. The algorithm also updates the information for the counters stored in USF. Function $ \getctr(i)$ returns the value of the counter for the i-th block in file $ \file$. When counters are used to protect against replay attacks, they need to be authenticated with a small amount of trusted storage. For authenticating block write counters, we define an algorithm AuthCtr that modifies the trusted storage space TSF of file $ \file$ to contain the trusted authentication information for the write counters of $ \file$, and a function CheckCtr that checks the authenticity of the counters stored in the trusted storage TSF for file $ \file$ and returns true if the counters are authentic and false, otherwise. Both operations for authenticating counters are invoked by an authorized client.


4.1 Storage and Authentication of Block Write Counters

A problem that needs to be addressed in the design of the various integrity algorithms described below is the storage and authentication of the block write counters. If a counter per file block were used, this would result in significant additional storage for counters. Here we propose a more efficient method of storing the block write counters, based on analyzing the file access patterns in NFS traces collected at Harvard University [9].

4.1.1 Counter intervals.

We performed experiments on the NFS Harvard traces [9] in order to analyze the file access patterns. We considered three different traces (LAIR, DEASNA and HOME02) for a period of one week. The LAIR trace consists of research workload traces from Harvard's computer science department. The DEASNA trace is a mix of research and email workloads from the division of engineering and applied sciences at Harvard. HOME02 is mostly the email workload from the campus general purpose servers.

Ellard et al. [9] make the observation that a large number of file accesses are sequential. This leads to the idea that the values of the write counters for adjacent blocks in a file might be correlated. To test this hypothesis, we represent counters for blocks in a file using counter intervals. A counter interval is defined as a sequence of consecutive blocks in a file that all share the same value of the write counter. For a counter interval, we need to store only the beginning and end of the interval, and the value of the write counter.

Table 1 shows the average storage per file used by the two counter representation methods for the three traces. We represent a counter using 2 bytes (as the maximum observed value of a counter was 9905) and we represent file block indices with 4 bytes. The counter interval method reduces the average storage needed for counters by a factor of 30 for the LAIR trace, 26.5 for the DEASNA trace and 7.66 for the HOME02 trace compared to the method that stores a counter per file block. This justifies our design choice to use counter intervals for representing counter values in the integrity algorithms presented in the next section.

Table 1: Average storage per file for two counter representation methods.
LAIR DEASNA HOME02
Counter per block 547.8 bytes 1.46 KB 3.16 KB
Counter intervals 18.35 bytes 55.04 bytes 413.44 bytes


4.1.2 Counter representation.

The counter intervals for file $ \file$ are represented by two arrays: IntStartF keeps the block indices where new counter intervals start and CtrValF keeps the values of the write counter for each interval. The trusted storage TSF for file $ \file$ includes either the arrays IntStartF and CtrValF if they fit into TSF, or, for each array, a hash of all its elements (concatenated), otherwise. In the limit, to reduce the bandwidth for integrity, we could build a Merkle tree to authenticate each of these arrays and store the root of these trees in TSF, but we have not seen in the Harvard traces files that would warrant this. We omit here the implementation details for the UpdateCtr, GetCtr, AuthCtr and CheckCtr algorithms on counters, due to space limitations.

If the counter intervals for a file get too dispersed, then the size of the arrays IntStartF and CtrValF might increase significantly. To keep the untrusted storage for integrity low, we could periodically change the encryption key for the file, re-encrypt all blocks in the file, and reset the block write counters to 0.


4.2 Length-Preserving Stateful Encryption with Counters

Secure encryption schemes are usually not length-preserving. However, one of our design goals stated in the introduction is to add security (and, in particular, encryption) to file systems in a manner transparent to the storage servers. For this purpose, we introduce here the notion of a length-preserving stateful encryption scheme for a file $ \file$, an encryption scheme that encrypts blocks in a way that preserves the length of the original blocks, and stores any additional information in the untrusted storage space for the file. We define a length-preserving stateful encryption scheme for a file $ \file$ to consist of a key generation algorithm Glen that generates an encryption key for the file, an encryption algorithm Elen that encrypts block content B for block index i with key $ k$ and outputs ciphertext C, and a decryption algorithm Dlen that decrypts the encrypted content C of block i with key $ k$ and outputs the plaintext B. Both the Elen and Dlen algorithms also modify the untrusted storage space for the file.

Tweakable ciphers are by definition length-preserving stateful encryption schemes. A different construction on which we elaborate below uses write counters for file blocks. Let $ (\gen,\enc,\dec)$ be an encryption scheme constructed from a block cipher in CBC mode. To encrypt an n-block message in the CBC encryption mode, a random initialization vector is chosen. The ciphertext consists of n+1 blocks, with the first being the initialization vector. We denote by $ \enc_k(\bc,iv)$ the output of the encryption of excluding the initialization vector) using key $ k$ and initialization vector iv, and similarly by $ \dec_k(\encb,iv)$ the decryption of key $ k$ and initialization vector iv.

We replace the random initialization vectors for encrypting a file block with a pseudorandom function application of the block index concatenated with the write counter for the block. This is intuitively secure because different initialization vectors are used for different encryptions of the same block, and moreover, the properties of pseudorandom functions imply that the initialization vectors are indistinguishable from random. It is thus enough to store the write counters for the blocks of a file, and the initialization vectors for the file blocks can be easily inferred.

The Glen, Elen and Dlen algorithms for a file $ \file$ are described in Figure 1. Here $ \pseudof:\keyspaceprf
\times \ctrset \rightarrow \keyspacebl$ denotes a pseudorandom function family with input space the set of all block indices concatenated with block counter values and output space the block space of E.

Figure 1: Implementing a length-preserving stateful encryption scheme with write counters.
len-enc


5 Integrity Constructions for Encrypted Storage

In this section, we first present a Merkle tree integrity construction for encrypted storage, used in file systems such as Cepheus [10], FARSITE [1], and Plutus [17]. Second, we introduce a new integrity construction based on tweakable ciphers that uses some ideas from Oprea et al. [26]. Third, we give a new construction based on compression levels of block contents. We evaluate the performance of the integrity algorithms described here in Section 7.

5.1 The Merkle Tree Construction MT-EINT

In this construction, file blocks can be encrypted with any length-preserving stateful encryption scheme and they are authenticated with a Merkle tree. More precisely, if $ F$ is a file comprised of blocks $ B_1,\dots,B_n$, then the untrusted storage for integrity for file $ \untr=\mtfile(h(1 \vert\vert B_1),\dots,h(n \vert\vert
B_n))$ (for $ h$ a second-preimage resistant hash function), and the trusted storage TSF is the root of this tree.

The algorithm Init runs the key generation algorithm Glen of the length-preserving stateful encryption scheme for file $ F$. The algorithms Update, Check, Append and Delete of the MT-EINT construction are given in Figure 2. We denote here by $ \file.\enckey$ the encryption key for file generated in the Init algorithm) and $ \file.\blocks$ the number of blocks in file $ F$.

- In the $ \update(i,\bc)$ algorithm for file $ F$, the i-th leaf in MTF is updated with the hash of the new block content using the algorithm UpdateTree and the encryption of B is stored in the i-th block of $ F$.

- To append a new block B to file $ F$ with algorithm $ \append(\bc)$, a new leaf is appended to MTF with the algorithm AppendTree, and then an encryption of B is stored in the n+1-th block of $ F$ (for n the number of blocks of $ F$).

- In the $ \checka(i,\encb)$ algorithm for file $ F$, block C is decrypted, and its integrity is checked using the CheckTree algorithm.

- To delete the last block from a file $ F$ with algorithm $ \delete$, the last leaf in MTF is deleted with the algorithm DeleteTree .

The MT-EINT construction detects data modification and block swapping attacks, as file block contents are authenticated by the root of the Merkle tree for each file. The MT-EINT is also secure against replay attacks, as the tree contains the hashes of the latest version of the data blocks and the root of the Merkle tree is authenticated in trusted storage.

Figure 2: The Update, Check, Append and Delete algorithms for the MT-EINT construction.

5.2 The Randomness Test Construction

Whereas in the Merkle tree construction any length-preserving stateful encryption algorithm can be used to individually encrypt blocks in a file, the randomness test construction uses the observation from Oprea et al. [26] that the integrity of the blocks that are efficiently distinguishable from random blocks can be checked with a randomness test if a tweakable cipher is used to encrypt them. As such, integrity information is stored only for random-looking blocks.

In this construction, a Merkle tree per file that authenticates the contents of the random-looking blocks is built. The untrusted storage for integrity USF for file $ \file$ comprised of blocks $ B_1,\dots,B_n$ includes this tree $ \randtree = \mt(h(i \vert\vert B_i) : i
\in \{1,\dots,n\}$    and $ \trand(B_i) = 1)$, and, in addition, the set of block numbers that are random-looking $ \offarr = \{i \in
\{1,\dots,n\} : \trand(B_i)=1\}$, ordered the same as the leaves in the previous tree RTreeF. The root of the tree RTreeF is kept in the trusted storage USF for file $ \file$.

To prevent against replay attacks, clients need to distinguish different writes of the same block in a file. A simple idea [26] is to use a counter per file block that denotes the number of writes of that block, and make the counter part of the encryption tweak. The block write counters need to be authenticated in the trusted storage space for the file $ \file$ to prevent clients from accepting valid older versions of a block that are considered not random by the randomness test. To ensure that file blocks are encrypted with different tweaks, we define the tweak for a file block to be a function of the file, the block index and the block write counter. We denote by $ \file.\tweakf$ the tweak-generating function for file takes as input a block index and a block counter and outputs the tweak for that file block. The properties of tweakable ciphers imply that if a block is decrypted with a different counter (and so a different tweak), then it will look random with high probability.

The algorithm Init selects a key at random from the key space of the tweakable encryption scheme E. The Update, Check, Append and Delete algorithms of RAND-EINT are detailed in Figure 3. For the array RArrF, $ \offarr.\items$ denotes the number of items in the array, $ \offarr.\last$ denotes the last element in the array, and the function $ \searchoff(i)$ for the array RArrF gives the position in the array where index i is stored (if it exists in the array).

- In the $ \update(i,\bc)$ algorithm for file $ \file$, the write counter for block i is incremented and the counter authentication information from TSF is updated with the algorithm AuthCtr. Then, the randomness test IsRand applied to block content B. If B is not random looking, then the leaf corresponding to block i ( if it exists) has to be removed from RTreeF. This is done with the algorithm $ \deleteofftree$, described in Figure 4. On the other hand, if B is random-looking, then the leaf corresponding to block i has to be either updated with the new hash (if it exists in the tree) or appended in RTreeF. Finally, the tweakable encryption of B is stored in the i-th block of $ \file$.

- To append a new block B to file $ \file$ with n blocks using the $ \append(\bc)$ algorithm, the counter for block n+1 is updated first with algorithm UpdateCtr. The counter authentication information from trusted storage is also updated with algorithm AuthCtr. Furthermore, the hash of the block index concatenated with the block content is added to RTreeF only if the block is random-looking. In addition, index n+1 is added to RArrF in this case. Finally, the tweakable encryption of B is stored in the n+1-th block of $ \file$.

- In the $ \checka(i,\encb)$ algorithm for file $ \file$, the authentication information for the block counters is checked first. Then block C is decrypted, and checked for integrity. If the content of the i-th block is not random-looking, then by the properties of tweakable ciphers we can infer that the block is valid with high probability. Otherwise, the integrity of the B-th block is checked using the tree RTreeF. If B is not a block index in the tree, then the integrity of block B is unconfirmed and the block is rejected.

- In the Delete algorithm for file $ \file$, the hash of the last block has to be removed from the tree by calling the algorithm DelOffsetTree (described in Figure 4), in the case in which the last block is authenticated through RTreeF.

It is not necessary to authenticate in trusted storage the array RArrF of indices of the random-looking blocks in a file. The reason is that the root of RTreeF is authenticated in trusted storage and this implies that an adversary cannot modify the order of the leaves in RTreeF without being detected in the AppendTree, UpdateTree or CheckTree algorithms.

The construction RAND-EINT protects against unauthorized modification of data written to disk and block swapping attacks by authenticating the root of RTreeF in the trusted storage space for each file. By using write counters in the encryption of block contents and authenticating the values of the counters in trusted storage, this construction provides defense against replay attacks and provides all the security properties of the MT-EINT construction.

Figure 3: The Update, Check, Append and Delete algorithms for the RAND-EINT construction.
Figure 4: The DelOffsetTree algorithm for a tree T deletes the hash of block i from T and moves the last leaf to its position, if necessary.

5.3 The Compression Construction

This construction is again based on the intuition that many workloads feature redundancy in file contents. In this construction, the block is compressed before encryption. If the compression level of the block content is high enough, then a message authentication code (i.e., MAC) of the block can be stored in the block itself, reducing the amount of storage necessary for integrity. The authentication information for blocks that can be compressed is stored on untrusted storage, and consequently a MAC is required. Like in the previous construction, a Merkle tree RTreeF is built over the hashes of the blocks in file $ \file$ that cannot be compressed enough, and the root of the tree is kept in trusted storage. In order to prevent replay attacks, it is necessary that block write counters are included either in the computation of the block MAC (in the case in which the block can be compressed) or in hashing the block (in the case in which the block cannot be compressed enough). Similarly to scheme RAND-EINT, the write counters for a file $ \file$ need to be authenticated in the trusted storage space TSF.

In this construction, file blocks can be encrypted with any length-preserving encryption scheme, as defined in Section 4.2. In describing the scheme, we need compression and decompression algorithms such that $ \decompress(\compress(m)) = m$, for any message m. We can also pad messages up to a certain fixed length by using the with an output of $ l$ bytes, and unpad a padded message with the such that $ \unpad(\pad(m)) = m$, for all messages m of length less than $ l$ bytes. We can use standard padding methods for implementing these algorithms [4]. To authenticate blocks that can be compressed, we use a message authentication code $ \hashff:\keyspacehash \times \{0,1\}^*
\rightarrow \{0,1\}^s$ that outputs strings of length s bits.

The algorithm Init runs the key generation algorithm Glen of the length-preserving stateful encryption scheme for file $ \file$ to generate key $ k_1$ and selects at random a key $ k_2$ from the key space KH of H. It outputs the tuple $ \la k_1,
k_2\ra$. The Update, Check, Append and Delete algorithms of the COMP-EINT construction are detailed in Figure 5. Here Lc is the byte length of the largest plaintext size for which the ciphertext is of length at most the file block length less the size of a MAC function output. For example, if the block size is 4096 bytes, HMAC [3] with SHA-1 is used for computing MACs (whose output is 20 bytes) and 16-byte AES is used for encryption, then Lc is the largest multiple of the AES block size (i.e., 16 bytes) less than 4096-20=4076 bytes. The value of Lc in this case is 4064 bytes.

- In the $ \update(i,\bc)$ algorithm for file $ \file$ , the write counter for block i is incremented and the counter authentication information from TSF is updated with the algorithm AuthCtr. Then block content B is compressed to $ \bc^c$. If the length of $ \bc^c$ (denoted $ \vert\bc^c\vert$) is at most Lc, then there is room to store the MAC of the block content inside the block. In this case, the hash of the previous block content stored at the same address is deleted from the Merkle tree RTreeF, if necessary. The compressed block is padded and encrypted, and then stored with its MAC in the i-th block of $ \file$. Otherwise, if the block cannot be compressed enough, then its hash has to be inserted into the Merkle tree RTreeF. The block content B is then encrypted with a length-preserving stateful encryption scheme using the key for the file and is stored in the i-th block of $ \file$.

- To append a new block B to file $ \file$ with n blocks using the $ \append(\bc)$ algorithm, the counter for block n+1 is updated first with algorithm UpdateCtr. The counter authentication information from trusted storage is also updated with algorithm AuthCtr. Block B is then compressed. If it has an adequate compression level, then the compressed block is padded and encrypted, and a MAC is concatenated at the end of the new block. Otherwise, a new hash is appended to the Merkle tree RTreeF and an encryption of B is stored in the n+1-th block of $ \file$.

- In the $ \checka(i,\encb)$ algorithm for file $ \file$, the authentication information from TSF for the block counters is checked first. There are two cases to consider. First, if the i-th block of $ \file$ is authenticated through the Merkle tree RTreeF, as indicated by RArrF, then the block is decrypted and algorithm CheckTree is called. Otherwise, the MAC of the block content is stored at the end of the block and we can thus parse the i-th block of $ \file$ as $ \encb'\vert\vert hval$. $ \encb'$ has to be decrypted, unpadded and decompressed, in order to obtain the original block content $ \bc_i$. The value $ hval$ stored in the block is checked to match the MAC of the block index i concatenated with the write counter for block i and block content $ \bc_i$.

- In the Delete algorithm for file $ \file$, the hash of the last block has to be removed from the tree by calling the algorithm DelOffsetTree described in Figure 4), in the case in which the last block is authenticated through RTreeF.

The construction against replay attacks by using write counters for either computing a MAC over the contents of blocks that can be compressed enough, or a hash over the contents of blocks that cannot be compressed enough, and authenticating the write counters in trusted storage. It meets all the security properties of and RAND-EINT.

Figure 5: The Update, Check, Append and Delete algorithms for the COMP-EINT construction.


6 Implementation

Our integrity algorithms are very general and they can be integrated into any cryptographic file system in either the kernel or user space. For the purpose of evaluating and comparing their performance, we implemented them in EncFS [14], an open-source user-level file system that transparently encrypts file blocks. EncFS uses the FUSE [12] library to provide the file system interface. FUSE provides a simple library API for implementing file systems and it has been integrated into recent versions of the Linux kernel.

In EncFS, files are divided into fixed-size blocks and each block is encrypted individually. Several ciphers such as AES and Blowfish in CBC mode are available for block encryption. We implemented in EncFS the three constructions that provide integrity: , RAND-EINT and COMP-EINT. While any length-preserving encryption scheme can be used in the and COMP-EINT constructions, RAND-EINT is constrained to use a tweakable cipher for encrypting file blocks. We choose to encrypt file blocks in MT-EINT and COMP-EINT with the length-preserving stateful encryption derived from the AES cipher in CBC mode (as shown in Section 4.2), and use the CMC tweakable cipher [15] as the encryption method in RAND-EINT . In our integrity algorithms, we use the SHA-1 hash function and the message-authentication code HMAC instantiated also with the SHA-1 hash function. For compressing and decompressing blocks in use the zlib library.

Our prototype architecture is depicted in Figure 6. We modified the user space of EncFS to include the CMC cipher for block encryption and the new integrity algorithms. The server uses the underlying file system (i.e., reiserfs) for the storage of the encrypted files. The Merkle trees for integrity RTreeF and the index arrays of the random-looking blocks RArrF are stored with the encrypted files in the untrusted storage space on the server. For faster integrity checking (in particular to improve the running time of the SearchOffset algorithm used in the Update and Check algorithms of the RAND-EINT and COMP-EINT constructions), we also keep the array RArrF for each file, ordered by indices. The roots of the trees RTreeF, and the arrays IntStartF and CtrValF or their hashes (if they are too large) are stored in a trusted storage space. In our current implementation, we use two extended attributes for each file $ F$, one for the root of RTreeF and the second for the arrays IntStartF and CtrValF or their hashes.

By default, EncFS caches the last block content written to or read from the disk. In our implementation, we cached the last arrays RArrF, IntStartF and CtrValF in a block update or check operation. Since these arrays are typically small (a few hundred bytes), they easily fit into memory. We also evaluate the effect of caching of Merkle trees in our system in Section 7.1.

Figure 6: Prototype architecture.


7 Performance Evaluation

In this section, we evaluate the performance of the new randomness test and compression integrity constructions for encrypted storage compared to that of Merkle trees. We ran our experiments on a 2.8 GHz Intel D processor machine with 1GB of RAM, running SuSE Linux 9.3 with kernel version 2.6.11. The hard disk used was an 80GB SATA 7200 RPM Maxtor.

The main challenge we faced in evaluating the proposed constructions was to come up with representative file system workloads. While the performance of the Merkle tree construction is predictable independently of the workload, the performance of the new integrity algorithms is highly dependent on the file contents accessed, in particular on the randomness of block contents. To our knowledge, there are no public traces that contain file access patterns, as well as the contents of the file blocks read and written. Due to the privacy implications of releasing actual users' data, we expect it to be nearly impossible to get such traces from a widely used system. However, we have access to three public NFS Harvard traces [9] that contain NFS traffic from several of Harvard's campus servers. The traces were collected at the level of individual NFS operations and for each read and write operation they contain information about the file identifier, the accessed offset in the file and the size of the request (but not the actual file contents).

To evaluate the integrity algorithms proposed in this paper, we perform two sets of experiments. In the first one, we strive to demonstrate how the performance of the new constructions varies for different file contents. For that, we use representative files from a Linux distribution installed on one of our desktop machines, together with other files from the user's home directory, divided into several file types. We identify five file types of interest: text, object, executables, images, and compressed files, and build a set of files for each class of interest. All files of a particular type are first encrypted and the integrity information for them is built; then they are decrypted and checked for integrity. We report the performance results for the files with the majority of blocks not random-looking (i.e., text, executable and object) and for those with mostly random-looking blocks (i.e., image and compressed). In this experiment, all files are written and read sequentially, and as such the access pattern is not a realistic one.

In the second set of experiments, we evaluate the effect of more realistic access patterns on the performance of the integrity schemes, using the NFS Harvard traces. As the Harvard traces do not contain information about actual file block contents written to the disks, we generate synthetic block contents for each block write request. We define two types of block contents: low-entropy and high-entropy, and perform experiments assuming that either all blocks are low-entropy or all are high-entropy. These extreme workloads represent the ``best'' and ``worst''-case for the new algorithms, respectively. We also consider a ``middle''-case, in which a block is random-looking with a 50% probability, and plot the performance results of the new schemes relative to the Merkle tree integrity algorithm for the best, middle and worst cases.


7.1 The Impact of File Block Contents on Integrity Performance

7.1.1 File sets.

We consider a snapshot of the file system from one of our desktop machines. We gathered files that belong to five classes of interest: (1) text files are files with extensions .txt, .tex, .c, .h, .cpp, .java, .ps, .pdf; (2) object files are system library files from the directory /usr/local/lib; (3) executable files are system executable files from directory /usr/local/bin; (4) image files are JPEG files and (5) compressed files are gzipped tar archives. Several characteristics of each set, including the total size, the number of files in each set, the minimum, average and maximum file sizes and the fraction of file blocks that are considered random-looking by the entropy test are given in Table 2.

Table 2: File set characteristics.
Total size No. files Min. file size Max. file size Avg. file size Fraction of random-looking blocks
Text 245 MB 808 27 bytes 34.94 MB 307.11 KB 0.0351
Objects 217 MB 28 15 bytes 92.66 MB 7.71 MB 0.0001
Executables 341 MB 3029 24 bytes 13.21 MB 112.84 KB 0.0009
Image 189 MB 641 17 bytes 2.24 MB 198.4 KB 0.502
Compressed 249 MB 2 80.44 MB 167.65 MB 124.05 MB 0.7812


7.1.2 Experiments.

We consider three cryptographic file systems: (1) MT-EINT with CBC-AES for encrypting file blocks; (2)RAND-EINT with CMC encryption; (3)COMP-EINT with CBC-AES encryption. For each cryptographic file system, we first write the files from each set; this has the effect of automatically encrypting the files, and running the Update algorithm of the integrity method for each file block. Second, we read all files from each set; this has the effect of automatically decrypting the files, and running the Check algorithm of the integrity method for each file block. We use file blocks of size 4KB in the experiments.

7.1.3 Micro-benchmarks.

We first present a micro-benchmark evaluation for the text and compressed file sets in Figure 7. We plot the total time to write and read the set of text and compressed files, respectively. The write time for a set of files includes the time to encrypt all the files in the set, create new files, write the encrypted contents in the new files and build the integrity information for each file block with algorithms Update and Append. The read time for a set of files includes the time to retrieve the encrypted files from disk, decrypt each file from the set and check the integrity of each file block with algorithm Check. We separate the total time incurred by the write and read experiments into the following components: encryption/decryption time (either AES or CMC); hashing time that includes the computation of both SHA-1 and HMAC; randomness check time (either the entropy test for RAND-EINT or compression/decompression time for COMP-EINT); Merkle tree operations (e.g., given a leaf index, find its index in inorder traversal or given an inorder index of a node in the tree, find the inorder index of its sibling and parent); the time to update and check the root of the tree (the root of the Merkle tree is stored as an extended attribute for the file) and disk waiting time.

Figure 7: Micro-benchmarks for text and compressed files.

The results show that the cost of CMC encryption and decryption is about 2.5 times higher than that of AES encryption and decryption in CBC mode. Decompression is between 4 and 6 times faster than compression and this accounts for the good read performance of COMP-EINT.

A substantial amount of the MT-EINT overhead is due to disk waiting time (for instance, 39% at read for text files) and the time to update and check the root of the Merkle tree (for instance, 30% at write for compressed files). In contrast, due to smaller sizes of the Merkle trees in the RAND-EINT and COMP-EINT file systems, the disk waiting time and the time to update and check the root of the tree for text files are smaller. The results suggests that caching of the hash values stored in Merkle trees in the file system might reduce the disk waiting time and the time to update the root of the tree and improve the performance of all three integrity constructions, and specifically that of the MT-EINT algorithm. We present our results on caching next.

7.1.4 Caching Merkle trees.

We implemented a global cache that stores the latest hashes read from Merkle trees used to either update or check the integrity of file blocks. As an optimization, when we verify the integrity of a file block, we compute all the hashes on the path from the node up to the root of the tree until we reach a node that is already in the cache and whose integrity has been validated. We store in the cache only nodes that have been verified and that are authentic. When a node in the cache is written, all its ancestors on the path from the node to the root, including the node itself, are evicted from the cache.

We plot the total file write and read time in seconds for the three cryptographic file systems as a function of different cache sizes. We also plot the average integrity bandwidth per block in a log-log scale. Finally, we plot the cumulative size of the untrusted storage for all files from each set. We show the combined graphs for low-entropy files (text, object and executable files) in Figure 8 and for high-entropy files (compressed and image files) in Figure 9.

The results show that MT-EINT benefits mostly on reads by implementing a cache of size 1KB, while the write time is not affected greatly by using a cache. The improvements for MT-EINT using a cache of 1KB are as much as 25.22% for low-entropy files and 20.34% for high-entropy files in the read experiment. In the following, we compare the performance of the three constructions for the case in which a 1KB cache is used.

7.1.5 Results for low-entropy files.

For sets of files with a low percent of random-looking blocks (text, object and executable files), RAND-EINT outperforms MT-EINT with respect to all the metrics considered. The performance of RAND-EINT compared to that of MT-EINT is improved by 31.77% for writes and 20.63% for reads. The performance of the COMP-EINT file system is very different in the write and read experiments due to the cost difference of compression and decompression. The write time of COMP-EINT is within 4% of the write time of MT-EINT and in the read experiment COMP-EINT outperforms MT-EINT by 25.27%. The integrity bandwidth of RAND-EINT and COMP-EINT is 92.93 and 58.25 times, respectively, lower than that of MT-EINT . The untrusted storage for integrity for RAND-EINT and COMP-EINT is reduced 2.3 and 1.17 times, respectively, compared to MT-EINT.

Figure 8: Evaluation for low-entropy files (text, object and executable files).

7.1.6 Results for high-entropy files.

For sets of files with a high percent of random-looking blocks (image and compressed files), RAND-EINT adds a maximum performance overhead of 4.43% for writes and 18.15% for reads compared to MT-EINT for a 1KB cache. COMP-EINT adds a write performance overhead of 38.39% compared to MT-EINT, and performs within 1% of MT-EINT in the read experiment. The average integrity bandwidth needed by RAND-EINT and COMP-EINT is lower by 30.15% and 10.22%, respectively, than that used by MT-EINT. The untrusted storage for integrity used by RAND-EINT is improved by 9.52% compared to MT-EINT and that of COMP-EINT is within 1% of the storage used by MT-EINT. The reason that the average integrity bandwidth and untrusted storage for integrity are still reduced in RAND-EINT compared to MT-EINT is that in the set of high-entropy files considered only about 70% of the blocks have high entropy. We would expect that for files with 100% high-entropy blocks, these two metrics will exhibit a small overhead with both RAND-EINT and COMP-EINT compared to MT-EINT (this is actually confirmed in the experiments from the next section). However, such workloads with 100% high entropy files are very unlikely to occur in practice.

Figure 9: Evaluation for high-entropy files (image and compressed files).


7.2 The Impact of File Access Patterns on Integrity Performance

7.2.1 File traces.

We considered a subset of the three NFS Harvard traces [9] (LAIR, DEASNA and HOME02), each collected during one day. We show several characteristics of each trace, including the number of files and the total number of block write and read operations, in Table 3. The block size in these traces is 4096 bytes and we have implemented a 1KB cache for Merkle trees.

Table 3: NFS Harvard trace characteristics.
Number of files Number of writes Number of reads
LAIR 7017 66331 23281
DEASNA 890 64091 521
HOME02 183 89425 11815


7.2.2 Experiments.

We replayed each of the three traces with three types of block contents: all low-entropy, all high-entropy and 50% high-entropy. For each experiment, we measured the total running time, the average integrity bandwidth and the total untrusted storage for integrity for RAND-EINT and COMP-EINT relative to MT-EINT and plot the results in Figure 10. We represent the performance of MT-EINT as the horizontal axis in these graphs and the performance of RAND-EINT and COMP-EINT relative to MT-EINT. The points above the horizontal axis are overheads compared to MT-EINT, and the points below the horizontal axis represent improvements relative to MT-EINT. The labels on the graphs denote the percent of random-looking blocks synthetically generated.

Figure 10: Running time, average integrity bandwidth and storage for integrity of RAND-EINT and COMP-EINT relative to MT-EINT. Labels on the graphs represent percentage of random-looking blocks.

7.2.3 Results.

The performance improvements of RAND-EINT and COMP-EINT compared to MT-EINT are as high as 56.21% and 56.85%, respectively, for the HOME02 trace for low-entropy blocks. On the other hand, the performance overhead for high-entropy blocks are at most 54.14% for RAND-EINT (in the LAIR trace) and 61.48% for COMP-EINT (in the DEASNA trace). RAND-EINT performs better than COMP-EINT when the ratio of read to write operations is small, as is the case for the DEASNA and HOME02 trace. As this ratio increases, COMP-EINT outperforms RAND-EINT.

For low-entropy files, both the average integrity bandwidth and the untrusted storage for integrity for both RAND-EINT and COMP-EINT are greatly reduced compared to MT-EINT. For instance, in the DEASNA trace, MT-EINT needs 215 bytes on average to update or check the integrity of a block, whereas RAND-EINT and COMP-EINT only require on average 0.4 bytes. The amount of additional untrusted storage for integrity in the DEASNA trace is 2.56 MB for MT-EINT and only 7 KB for RAND-EINT and COMP-EINT. The maximum overhead added by both RAND-EINT and COMP-EINT compared to MT-EINT for high-entropy blocks is 30.76% for the average integrity bandwidth (in the HOME02 trace) and 19.14% for the amount of untrusted storage for integrity (in the DEASNA trace).


7.3 Discussion

From the evaluation of the three constructions, it follows that none of the schemes is a clear winner over the others with respect to all the four metrics considered. Since the performance of both RAND-EINT and COMP-EINT is greatly affected by file block contents, it would be beneficial to know the percentage of high-entropy blocks in practical filesystem workloads. To determine statistics on file contents, we have performed a user study on several machines from our department running Linux. For each user machine, we have measured the percent of high-entropy blocks and the percent of blocks that cannot be compressed enough from users' home directories. The results show that on average, 28% percent of file blocks have high entropy and 32% percent of file blocks cannot be compressed enough to fit a MAC inside.

The implications of our study are that, for cryptographic file systems that store files similar to those in users' home directories, the new integrity algorithms improve upon Merkle trees with respect to all four metrics of interest. In particular, COMP-EINT is the best option for primarily read-only workloads when minimizing read latency is a priority, and RAND-EINT is the best choice for most other workloads. On the other hand, for an application in which the large majority of files have high-entropy (e.g., a file sharing application in which users transfer mostly audio and video files), the standard MT-EINT still remains the best option for integrity. We recommend that all three constructions be implemented in a cryptographic file system. An application can choose the best scheme based on its typical workload.

The new algorithms that we propose can be applied in other settings in which authentication of data stored on untrusted storage is desired. One example is checking the integrity of arbitrarily-large memory in a secure processor using only a constant amount of trusted storage [5,7]. In this setting, a trusted checker maintains a constant amount of trusted storage and, possibly, a cache of data blocks most recently read from the main memory. The goal is for the checker to verify the integrity of the untrusted memory using a small bandwidth overhead.

The algorithms described in this paper can be used only in applications where the data that needs to be authenticated is encrypted. However, the COMP-EINT algorithm can be easily modified to fit into a setting in which data is only authenticated and not encrypted, and can thus replace Merkle trees in such applications. On the other hand, the RAND-EINT integrity algorithm is only suitable in a setting in which data is encrypted with a tweakable cipher, as the integrity guarantees of this algorithm are based on the security properties of such ciphers.


8 Related Work

Cryptographic file systems extend traditional file systems with support for security in untrusted storage environments. Most cryptographic file systems provide to their clients mechanisms for encrypting data and checking data integrity. The cryptographic operations are performed on the client side, as both the storage devices and the network are untrusted.

We have focused on Merkle trees as our point of comparison, though there are other integrity protections used on various cryptographic file systems that we have elided due to their greater expense in various measures. For example, a common integrity method used in cryptographic file systems such as TCFS [6] and SNAD [25] is to store a hash or message authentication code for each file block for authenticity. However, these approaches employ trusted storage linear in the number of blocks in the file (either the hashes or a counter per block). In systems such as SFS [22], SFSRO [11], Cepheus [10], FARSITE [1], Plutus [17], SUNDR [20] and IBM StorageTank [23,27], a Merkle tree per file is built and the root of the tree is authenticated (by either digitally signing it or storing it in a trusted meta-data server). In SiRiUS [13], each file is digitally signed for authenticity, and so in addition the integrity bandwidth to update or check a block in a file is linear in the file size. Tripwire [19] is a user-level tool that computes a hash per file and stores it in trusted storage. While this approach achieves constant trusted storage for integrity per file, the integrity bandwidth is linear in the number of blocks in the file. For journaling file systems, an elegant solution for integrity called hash logging is provided by PFS [32]. The hashes of file blocks together with the file system metadata are stored in the file system log, a protected memory area. However, in this solution the amount of trusted storage for integrity for a file is linear in the number of blocks in the file.

Pletka and Cachin [27] describes a security architecture for the distributed IBM StorageTank file system (also denoted the SAN.FS file system) [23]. SAN.FS achieves its scalability to many clients by separating the meta-data operations from the data path. As such, the meta-data is stored on trusted meta-data servers, while the data is stored on the storage devices from a SAN. SAN.FS uses AES in CBC mode for confidentiality and Merkle trees (with degree 16) for integrity protection of files. The roots of the Merkle trees are stored together with the file system meta-data on the meta-data servers. We believe that our techniques could be used to good effect in this system, as well.

Riedel et al. [28] provides a framework for extensively evaluating the security of storage systems. Wright et al. [33] evaluates the performance of five cryptographic file systems, focusing on the overhead of encryption. Two other recent surveys about securing storage systems are by Sivathanu et al. [31] and Kher and Kimand [18].

Another area related to our work is that of checking the integrity of arbitrarily large untrusted memory using only a small, constant-size trusted memory. A first solution to this problem [5] is to check the integrity of each memory operation using a Merkle tree. This results in a logarithmic bandwidth overhead in the total number of memory blocks. Recent solutions try to reduce the logarithmic bandwidth to almost a constant value. The main idea by Clarke et al. [7] is to verify the integrity of the memory only when a critical operation is encountered. The integrity of sequences of operations between critical operations is checked by aggregating these operations in a log using incremental hash functions []. In contrast, in our model we need to be able to check the integrity of each file block read from the storage servers.

9 Conclusion

We have proposed two new integrity constructions, RAND-EINT and COMP-EINT, that authenticate file blocks in a cryptographic file system using only a constant amount of trusted storage per file. Our constructions exploit the typical low entropy of block contents and sequentiality of file block writes to reduce the additional costs of integrity protection. We have evaluated the performance of the new constructions relative to the widely used Merkle tree algorithm, using files from a standard Linux distribution and NFS traces collected at Harvard university. Our experimental evaluation demonstrates that the performance of the new algorithms is greatly affected by file block contents and file access patterns. For workloads with majority low-entropy file blocks, the new algorithms improve upon Merkle trees with respect to all the four metrics considered.

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About this document ...

Integrity Checking in Cryptographic File Systems
with Constant Trusted Storage

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Footnotes

... Oprea1
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA; alina@cs.cmu.edu
... Reiter2
Electrical & Computer Engineering Department and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA; reiter@cmu.edu

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