Big Data Analytics over Encrypted Datasets with Seabed


Antonis Papadimitriou, University of Pennsylvania and Microsoft Research India; Ranjita Bhagwan, Nishanth Chandran, and Ramachandran Ramjee, Microsoft Research India; Andreas Haeberlen, University of Pennsylvania; Harmeet Singh and Abhishek Modi, Microsoft Research India; Saikrishna Badrinarayanan, University of California, Los Angeles and Microsoft Research India


Today, enterprises collect large amounts of data and leverage the cloud to perform analytics over this data. Since the data is often sensitive, enterprises would prefer to keep it confidential and to hide it even from the cloud operator. Systems such as CryptDB and Monomi can accomplish this by operating mostly on encrypted data; however, these systems rely on expensive cryptographic techniques that limit performance in true “big data” scenarios that involve terabytes of data or more.

This paper presents Seabed, a system that enables efficient analytics over large encrypted datasets. In contrast to previous systems, which rely on asymmetric encryption schemes, Seabed uses a novel, additively symmetric homomorphic encryption scheme (ASHE) to perform large-scale aggregations efficiently. Additionally, Seabed introduces a novel randomized encryption scheme called Splayed ASHE, or SPLASHE, that can, in certain cases, prevent frequency attacks based on auxiliary data.

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@inproceedings {199372,
author = {Antonis Papadimitriou and Ranjita Bhagwan and Nishanth Chandran and Ramachandran Ramjee and Andreas Haeberlen and Harmeet Singh and Abhishek Modi and Saikrishna Badrinarayanan},
title = {Big Data Analytics over Encrypted Datasets with Seabed},
booktitle = {12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)},
year = {2016},
isbn = {978-1-931971-33-1},
address = {Savannah, GA},
pages = {587--602},
url = {},
publisher = {{USENIX} Association},