Computing over Distributed Sensitive Data: Publications

2017
K. Nissim and U. Stemmer. 3/2017. “Concentration Bounds for High Sensitivity Functions Through Differential Privacy”. arXiv VersionAbstract

A new line of work [6, 9, 15, 2] demonstrates how differential privacy [8] can be used as a mathematical tool for guaranteeing generalization in adaptive data analysis. Specifically, if a differentially private analysis is applied on a sample S of i.i.d. examples to select a lowsensitivity function f , then w.h.p. f (S) is close to its expectation, although f is being chosen based on the data. Very recently, Steinke and Ullman [16] observed that these generalization guarantees can be used for proving concentration bounds in the non-adaptive setting, where the low-sensitivity function is fixed beforehand. In particular, they obtain alternative proofs for classical concentration bounds for low-sensitivity functions, such as the Chernoff bound and McDiarmid’s Inequality. In this work, we set out to examine the situation for functions with high-sensitivity, for which differential privacy does not imply generalization guarantees under adaptive analysis. We show that differential privacy can be used to prove concentration bounds for such functions in the non-adaptive setting.

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E. Cicek, G. Barthe, M. Gaboardi, D. Garg, and J. Hoffmann. 1/2017. “Relational Cost Analysis.” Symposium on the Principle of Programming Languages, ACM.
A. Azevedo de Amorim, M. Gaboardi, J. Hsu, S. Katsumata, and I. Cherigui. 1/2017. “A Semantic Account of Metric Preservation.” Symposium on the Principle of Programming Languages, ACM.
S. Kasiviswanathan, K. Nissim, and H. Jin. 2017. “Private Incremental Regression.” PODS.
2016
G. Barthe, M. Gaboardi, E. J. Gallego Arias, J. Hsu, A. Roth, and P.-Y. Strub. 12/2016. “Computer-Aided Verification in Mechanism Design.” Conference on Internet and Economics, WINE .
G. Barthe, N. Fong, M. Gaboardi, B. Gregoire, J. Hsu, and P.-Y. Strub. 10/2016. “Advanced Probabilistic Couplings for Differential Privacy .” 23rd ACM Conference on Computer and Communications Security, CCS.
G. Barthe, G. P. Farina, M. Gaboardi, E. J. Gallego Arias, A. D. Gordon, J. Hsu, and P.-Y. Strub. 10/2016. “Differentially Private Bayesian Programming .” 23rd ACM Conference on Computer and Communications Security, CCS.
Georgios Kellaris, George Kollios, Kobbi Nissim, and Adam O'Neill. 10/2016. “Generic Attacks on Secure Outsourced Databases.” 23rd ACM Conference on Computer and Communications Security.Abstract

Recently, various protocols have been proposed for securely outsourcing database storage to a third party server, ranging from systems with “full-fledged” security based on strong cryptographic primitives such as fully homomorphic encryption or oblivious RAM, to more practical implementations based on searchable symmetric encryption or even on deterministic and order-preserving encryption. On the flip side, various attacks have emerged that show that for some of these protocols confidentiality of the data can be compromised, usually given certain auxiliary information. We take a step back and identify a need for a formal understanding of the inherent efficiency/privacy trade-off in outsourced database systems, independent of the details of the system. We propose abstract models that capture secure outsourced storage systems in sufficient generality, and identify two basic sources of leakage, namely access pattern and communication volume. We use our models to distinguish certain classes of outsourced database systems that have been proposed, and deduce that all of them exhibit at least one of these leakage sources. We then develop generic reconstruction attacks on any system supporting range queries where either access pattern or communication volume is leaked. These attacks are in a rather weak passive adversarial model, where the untrusted server knows only the underlying query distribution. In particular, to perform our attack the server need not have any prior knowledge about the data, and need not know any of the issued queries nor their results. Yet, the server can reconstruct the secret attribute of every record in the database after about N 4 queries, where N is the domain size. We provide a matching lower bound showing that our attacks are essentially optimal. Our reconstruction attacks using communication volume apply even to systems based on homomorphic encryption or oblivious RAM in the natural way. Finally, we provide experimental results demonstrating the efficacy of our attacks on real datasets with a variety of different features. On all these datasets, after the required number of queries our attacks successfully recovered the secret attributes of every record in at most a few seconds.

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