Batch PIR and Labeled PSI with Oblivious Ciphertext Compression


Alexander Bienstock, New York University; Sarvar Patel and Joon Young Seo, Google; Kevin Yeo, Google and Columbia University


In this paper, we study two problems: oblivious compression and decompression of ciphertexts. In oblivious compression, a server holds a set of ciphertexts with a subset of encryptions of zeroes whose positions are only known to the client. The goal is for the server to effectively compress the ciphertexts obliviously, while preserving the non-zero plaintexts and without learning the plaintext values. For oblivious decompression, the client, instead, succinctly encodes a sequence of plaintexts such that the server may decode encryptions of all plaintexts value, but the zeroes may be replaced with arbitrary values. We present solutions to both problems that construct lossless compressions as small as only 5% more than the optimal minimum using only additive homomorphism. The crux of both algorithms involve embedding ciphertexts as random linear systems that are efficiently solvable.

Using our compression schemes, we obtain state-of-the-art schemes for batch private information retrieval (PIR) where a client wishes to privately retrieve multiple entries from a server-held database in one query. We show that our compression schemes may be used to reduce communication by up to 30% for batch PIR in both the single and two-server settings.

Additionally, we study labeled private set intersection (PSI) in the unbalanced setting where one party's set is significantly smaller than the other party's set and each entry has associated data. By utilizing our novel compression algorithm, we present a protocol with 65-88% reduction in communication with comparable computation compared to prior works.

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