In PETS 2015, Kiayias, Leonardos, Lipmaa, Pavlyk, and Tang proposed the first (n, 1)-CPIR protocol with rate 1−𝑜(1). They use advanced techniques from multivariable calculus (like the Newton-Puiseux algorithm) to establish optimal rate among a large family of different CPIR protocols. It is only natural to ask whether one can achieve similar rate but with a much simpler analysis. We propose parameters to the earlier (n, 1)-CPIR protocol of Lipmaa (ISC 2005), obtaining a CPIR protocol that is asymptotically almost as communication-efficient as the protocol of Kiayias et al. However, for many relevant parameter choices, it is slightly more communication-efficient, due to the cumulative rounding errors present in the protocol of Kiayias et al. Moreover, the new CPIR protocol is simpler to understand, implement, and analyze. The new CPIR protocol can be used to implement (computationally inefficient) FHE with rate 1−𝑜(1).
Private information retrieval (PIR) is a fundamental tool for preserving query privacy when accessing outsourced data. All previous PIR constructions have significant costs preventing widespread use. In this work, we present private stateful information retrieval (PSIR), an extension of PIR, allowing clients to be stateful and maintain information between multiple queries. Our design of the PSIR primitive maintains three important properties of PIR: multiple clients may simultaneously query without complex concurrency primitives, query privacy should be maintained if the server colludes with other clients, and new clients should be able to enroll into the system by exclusively interacting with the server.
We present a PSIR framework that reduces an online query to performing one single-server PIR on a sub-linear number of database records. All other operations beyond the single-server PIR consist of cryptographic hashes or plaintext operations. In practice, the dominating costs of resources occur due to the public-key operations involved with PIR. By reducing the input database to PIR, we are able to limit expensive computation and avoid transmitting large ciphertexts. We show that various instantiations of PSIR reduce server CPU by up to 10x and online network costs by up to 10x over the previous best PIR construction.
We show a protocol for two-server oblivious RAM (ORAM) that is simpler and more efficient than the best prior work. Our construction combines any tree-based ORAM with an extension of a two-server private information retrieval scheme by Boyle et al., and is able to avoid recursion and thus use only one round of interaction. In addition, our scheme has a very cheap initialization phase, making it well suited for RAM-based secure computation. Although our scheme requires the servers to perform a linear scan over the entire data, the cryptographic computation involved consists only of block-cipher evaluations.
A practical instantiation of our protocol has excellent concrete parameters: for storing an N
-element array of arbitrary size data blocks with statistical security parameter λ, the servers each store 4N encrypted blocks, the client stores λ+2logN blocks, and the total communication per logical access is roughly 10logN encrypted blocks.
Private information retrieval (PIR) is a key building block in many privacy-preserving systems. Unfortunately, existing constructions remain very expensive. This paper introduces two techniques that make the computational variant of PIR (CPIR) more efficient in practice. The first technique targets a recent class of CPU-efficient CPIR protocols where the query sent by the client contains a number of ciphertexts proportional to the size of the database. We show how to compresses this query, achieving size reductions of up to 274X. The second technique is a new data encoding called probabilistic batch codes (PBCs). We use PBCs to build a multi query PIR scheme that allows the server to amortize its computational cost when processing a batch of requests from the same client. This technique achieves up to 40× speedup over processing queries one at a time, and is significantly more efficient than related encodings. We apply our techniques to the Pung private communication system, which relies on a custom multi-query CPIR protocol for its privacy guarantees. By porting our techniques to Pung, we find that we can simultaneously reduce network costs by 36× and increase throughput by 3X.