Optimal Rate-Distortion-Leakage Tradeoff for Single-Server Information Retrieval

Private information retrieval protocols guarantee that a user can privately and losslessly retrieve a single file from a database stored across multiple servers. In this work, we propose to simultaneously relax the conditions of perfect retrievability and privacy in order to obtain improved download rates in the single server scenario, i.e., all files are stored uncoded on a single server. In particular, we derive the optimal tradeoff between download rate, distortion, and information leakage when the file size is infinite and the information leakage is measured in terms of the average success probability for the server of correctly guessing the identity of the requested file. Moreover, we present a novel approach based on linear programming to construct schemes for a finite file size and an arbitrary number of files. When the database contains at most four bits, this approach can be leveraged to find provably optimal schemes.

[1]  Tomohiko Uyematsu,et al.  Non-asymptotic bounds for fixed-length lossy compression , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[2]  Thomas M. Cover,et al.  Elements of information theory (2. ed.) , 2006 .

[3]  Rafail Ostrovsky,et al.  Replication is not needed: single database, computationally-private information retrieval , 1997, Proceedings 38th Annual Symposium on Foundations of Computer Science.

[4]  Hsuan-Yin Lin,et al.  Weak Flip Codes and their Optimality on the Binary Erasure Channel , 2017, IEEE Transactions on Information Theory.

[5]  Eitan Yaakobi,et al.  Multi-Server Weakly-Private Information Retrieval , 2022, IEEE Transactions on Information Theory.

[6]  Sennur Ulukus,et al.  Semantic Private Information Retrieval , 2020, IEEE Transactions on Information Theory.

[7]  Sergio Verdú,et al.  Fixed-Length Lossy Compression in the Finite Blocklength Regime , 2011, IEEE Transactions on Information Theory.

[8]  Hirosuke Yamamoto,et al.  Private information retrieval for coded storage , 2014, 2015 IEEE International Symposium on Information Theory (ISIT).

[9]  Chao Tian,et al.  Weakly Private Information Retrieval Under the Maximal Leakage Metric , 2020, 2020 IEEE International Symposium on Information Theory (ISIT).

[10]  Geoffrey Smith,et al.  On the Foundations of Quantitative Information Flow , 2009, FoSSaCS.

[11]  Sudeep Kamath,et al.  An Operational Approach to Information Leakage , 2018, IEEE Transactions on Information Theory.

[12]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[13]  Raymond W. Yeung,et al.  Information Theory and Network Coding , 2008 .

[14]  Michael Gastpar,et al.  Single-server Multi-user Private Information Retrieval with Side Information , 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

[15]  Camilla Hollanti,et al.  Private Information Retrieval from Coded Databases with Colluding Servers , 2016, SIAM J. Appl. Algebra Geom..

[16]  Fatemeh Kazemi,et al.  The Role of Coded Side Information in Single-Server Private Information Retrieval , 2019, IEEE Transactions on Information Theory.

[17]  Eitan Yaakobi,et al.  The Capacity of Single-Server Weakly-Private Information Retrieval , 2020, IEEE Journal on Selected Areas in Information Theory.

[18]  R. Gray,et al.  A new class of lower bounds to information rates of stationary sources via conditional rate-distortion functions , 1973, IEEE Trans. Inf. Theory.

[19]  Eyal Kushilevitz,et al.  Private information retrieval , 1998, JACM.

[20]  H. Vincent Poor,et al.  Channel Coding Rate in the Finite Blocklength Regime , 2010, IEEE Transactions on Information Theory.

[21]  Hsuan-Yin Lin,et al.  Achieving Maximum Distance Separable Private Information Retrieval Capacity With Linear Codes , 2017, IEEE Transactions on Information Theory.

[22]  Eitan Yaakobi,et al.  Weakly-Private Information Retrieval , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

[23]  Loukas Lazos,et al.  On the Capacity of Leaky Private Information Retrieval , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

[24]  Hsuan-Yin Lin,et al.  Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval , 2020, ArXiv.

[25]  Sennur Ulukus,et al.  The Capacity of Private Information Retrieval From Coded Databases , 2016, IEEE Transactions on Information Theory.

[26]  Swanand Kadhe,et al.  Private Information Retrieval With Side Information , 2017, IEEE Transactions on Information Theory.

[27]  Henry Corrigan-Gibbs,et al.  Private Information Retrieval with Sublinear Online Time , 2020, IACR Cryptol. ePrint Arch..

[28]  Hua Sun,et al.  The Capacity of Private Information Retrieval , 2017, IEEE Transactions on Information Theory.

[29]  Loukas Lazos,et al.  Asymmetric Leaky Private Information Retrieval , 2020, IEEE Transactions on Information Theory.

[30]  Helger Lipmaa,et al.  A Simpler Rate-Optimal CPIR Protocol , 2017, Financial Cryptography.

[31]  R. Gray Conditional Rate-Distortion Theory , 1972 .

[32]  Zeev Dvir,et al.  2-Server PIR with Subpolynomial Communication , 2016, J. ACM.

[33]  D. Griffel Linear programming 2: Theory and extensions , by G. B. Dantzig and M. N. Thapa. Pp. 408. £50.00. 2003 ISBN 0 387 00834 9 (Springer). , 2004, The Mathematical Gazette.

[34]  Salim El Rouayheb,et al.  Private Information Retrieval From MDS Coded Data in Distributed Storage Systems , 2016, IEEE Transactions on Information Theory.

[35]  Karim A. Banawan,et al.  Semantic Private Information Retrieval: Effects of Heterogeneous Message Sizes and Popularities , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.