Computing over Distributed Sensitive Data: Publications

2020
Benny Applebaum, Amos Beimel, Oded Nir, and Naty Peter. 6/2020. “Better Secret-Sharing via Robust Conditional Disclosure of Secrets.” In 52nd ACM Symposium on Theory of Computing (To appear - STOC 2020). ePrint VersionAbstract

A secret-sharing scheme allows to distribute a secret s among n parties such that only some predefined

“authorized” sets of parties can reconstruct the secret, and all other “unauthorized” sets learn

nothing about s. The collection of authorized sets is called the access structure. For over 30 years, it

was known that any (monotone) collection of authorized sets can be realized by a secret-sharing scheme

whose shares are of size 2n-o(n)and until recently no better scheme was known. In a recent breakthrough,

Liu and Vaikuntanathan (STOC 2018) have reduced the share size to 20:994n+o(n), which was

later improved to 20:892n+o(n)by Applebaum et al. (EUROCRYPT 2019).

 

In this paper we improve the exponent of general secret-sharing down to 0:637. For the special case

of linear secret-sharing schemes, we get an exponent of 0:762 (compared to 0:942 of Applebaum et al.).

As our main building block, we introduce a new robust variant of conditional disclosure of secrets

(robust CDS) that achieves unconditional security even under limited form of re-usability. We show that

the problem of general secret-sharing reduces to robust CDS with sub-exponential overhead and derive

our main result by implementing robust CDS with a non-trivial exponent. The latter construction follows

by presenting a general immunization procedure that turns standard CDS into a robust CDS.

ePrint-Jan 2020.pdf ePrint-May 2020.pdf
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, and Uri Stemmer. 6/2020. “The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers.” In Information-Theoretic Cryptography (To appear - ITC 2020). ArXiv VersionAbstract

Motivated by the desire to bridge the utility gap between local and trusted curator modelsof differential privacy for practical applications, we initiate the theoretical study of a hybridmodel introduced by “Blender” [Avent et al., USENIX Security ’17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users. We focus on the regime where mn and study the new capabilities of this (m;n)-hybrid model. We show that, despite the fact that the hybrid model adds no significant new capabilities for the basic task of simple hypothesistesting, there are many other tasks (under a wide range of parameters) that can be solved in the hybrid model yet cannot be solved either by the curator or by the local-users separately. Moreover, we exhibit additional tasks where at least one round of interaction between the curator and the local-users is necessary – namely, no hybrid model protocol without such interaction can solve these tasks. Taken together, our results show that the combination of the local model with a small curator can become part of a promising toolkit for designing and implementing differential privacy.

ARXIV 2019.pdf
Victor Balcer and Albert Cheu. 4/2020. “Separating Local & Shuffled Differential Privacy via Histograms.” In Information-Theoretic Cryptography (To appear - ITC 2020). ArXiv VersionAbstract

Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users’ hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This impliesan arbitrarily large gap in sample complexity between the shuffled and local models. On theother hand, we show that the models are equivalent when we impose the constraints of pure differential privacy and single-message randomizers.

ARXIV 2019.pdf
Owen Arden, Anitha Gollamudi, Ethan Cecchetti, Stephen Chong, and Andrew C. Myers. 2020. “A Calculus for Flow-Limited Authorization.” Journal of Computer Security.Abstract

Real-world applications routinely make authorization decisions based on dynamic computation. Reasoning about dynamically computed authority is challenging. Integrity of the system might be compromised if attackers can improperly influence the authorizing computation. Confidentiality can also be compromised by authorization, since authorization decisions are often based on sensitive data such as membership lists and passwords. Previous formal models for authorization do not fully address the security implications of permitting trust relationships to change, which limits their ability to reason about authority that derives from dynamic computation. Our goal is a way to construct dynamic authorization mechanisms that do not violate confidentiality or integrity.

We introduce the Flow-Limited Authorization Calculus (FLAC), which is both a simple, expressive model for reasoning about dynamic authorization and also an information flow control language for securely implementing various authorization mechanisms. FLAC combines the insights of two previous models: it extends the Dependency Core Calculus with features made possible by the Flow-Limited Authorization Model. FLAC provides strong end-to-end information security guarantees even for programs that incorporate and implement rich dynamic authorization mechanisms. These guarantees include noninterference and robust declassification, which prevent attackers from influencing information disclosures in unauthorized ways. We prove these security properties formally for all FLAC programs and explore the expressiveness of FLAC with several examples.

JCS 2020 Submitted.pdf CSF 2016.pdf
2019
Amos Beimel, Kobbi Nissim, and Mohammad Zaherix. 10/2019. “Exploring Differential Obliviousness.” In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 10/2019. Publisher's VersionAbstract

In a recent paper, Chan et al. [SODA ’19] proposed a relaxation of the notion of (full) memory obliviousness, which was introduced by Goldreich and Ostrovsky [J. ACM ’96] and extensively researched by cryptographers. The new notion, differential obliviousness, requires that any two neighboring inputs exhibit similar memory access patterns, where the similarity requirement is that of differential privacy.

Chan et al. demonstrated that differential obliviousness allows achieving improved efficiency for several algorithmic tasks, including sorting, merging of sorted lists, and range query data structures.

In this work, we continue the exploration of differential obliviousness, focusing on algorithms that do not necessarily examine all their input. This choice is motivated by the fact that the existence of logarithmic overhead ORAM protocols implies that differential obliviousness can yield at most a logarithmic improvement in efficiency for computations that need to examine all their input. In particular, we explore property testing, where we show that differential obliviousness yields an almost linear improvement in overhead in the dense graph model, and at most quadratic improvement in the bounded degree model.

We also explore tasks where a non-oblivious algorithm would need to explore different portions of the input, where the latter would depend on the input itself, and where we show that such a behavior can be maintained under differential obliviousness, but not under full obliviousness. Our examples suggest that there would be benefits in further exploring which class of computational tasks are amenable to differential obliviousness.

RANDOM 2019 PDF ARXIV 2019.pdf
Gian Pietro Farina, Stephen Chong, and Marco Gaboardi. 10/2019. “Relational Symbolic Execution.” In 21st International Symposium on Principles and Practice of Declarative Programming (PPDP 2019). Publisher's VersionAbstract

Symbolic execution is a classical program analysis technique used to show that programs satisfy or violate given specifications. In this work we generalize symbolic execution to support program analysis for relational specifications in the form of relational properties - these are properties about two runs of two programs on related inputs, or about two executions of a single program on related inputs. Relational properties are useful to formalize notions in security and privacy, and to reason about program optimizations. We design a relational symbolic execution engine, named RelSymwhich supports interactive refutation, as well as proving of relational properties for programs written in a language with arrays and for-like loops.

PPDP 2019.pdf
Victor Balcer and Salil Vadhan. 9/2019. “Differential Privacy on Finite Computers.” Journal of Privacy and Confidentiality, 9, 2. JPC PageAbstract

Version History: 

Also presented at TPDP 2017; preliminary version posted as arXiv:1709.05396 [cs.DS].

2018: Published in Anna R. Karlin, editor, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018), volume 94 of Leibniz International Proceedings in Informatics (LIPIcs), pp 43:1-43:21. http://drops.dagstuhl.de/opus/frontdoor.php?source_opus=8353

We consider the problem of designing and analyzing differentially private algorithms that can be implemented on discrete models of computation in strict polynomial time, motivated by known attacks on floating point implementations of real-arithmetic differentially private algorithms (Mironov, CCS 2012) and the potential for timing attacks on expected polynomial-time algorithms. As a case study, we examine the basic problem of approximating the histogram of a categorical dataset over a possibly large data universe \(X\). The classic Laplace Mechanism (Dwork, McSherry, Nissim, Smith, TCC 2006 and J. Privacy & Condentiality 2017) does not satisfy our requirements, as it is based on real arithmetic, and natural discrete analogues, such as the Geometric Mechanism (Ghosh, Roughgarden, Sundarajan, STOC 2009 and SICOMP 2012), take time at least linear in \(|X|\), which can be exponential in the bit length of the input.

In this paper, we provide strict polynomial-time discrete algorithms for approximate histograms whose simultaneous accuracy (the maximum error over all bins) matches that of the Laplace Mechanism up to constant factors, while retaining the same (pure) differential privacy guarantee.  One of our algorithms produces a sparse histogram as output. Its "per-bin accuracy" (the error on individual bins) is worse than that of the Laplace Mechanism by a factor of \(\log |X|\), but we prove a lower bound showing that this is necessary for any algorithm that produces a sparse histogram.  A second algorithm avoids this lower bound, and matches the per-bin accuracy of the Laplace Mechanism, by producing a compact and eciently computable representation of a dense histogram; it is based on an \((n + 1)\) - wise independent implementation of an appropriately clamped version of the Discrete Geometric Mechanism.

 

JPC 2019 ITCS 2018 ArXiv
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim. 6/2/2019. “The Privacy Blanket of the Shuffle Model.” In International Cryptology Conference (CRYPTO 2019). Publisher's VersionAbstract
This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields a trust model which sits in between the classical curator and local models for differential privacy. The shuffle model is the core idea in the Encode, Shuffle, Analyze (ESA) model introduced by Bittau et al. (SOPS 2017). Recent work by Cheu et al. (EUROCRYPT 2019) analyzes the differential privacy properties of the shuffle model and shows that in some cases shuffled protocols provide strictly better accuracy than local protocols. Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user. In this context, we make three contributions. First, we provide an optimal single message protocol for summation of real numbers in the shuffle model. Our protocol is very simple and has better accuracy and communication than the protocols for this same problem proposed by Cheu et al. Optimality of this protocol follows from our second contribution, a new lower bound for the accuracy of private protocols for summation of real numbers in the shuffle model. The third contribution is a new amplification bound for analyzing the privacy of protocols in the shuffle model in terms of the privacy provided by the corresponding local randomizer. Our amplification bound generalizes the results by Erlingsson et al. to a wider range of parameters, and provides a whole family of methods to analyze privacy amplification in the shuffle model.
ARXIV 2019 .pdf
Anitha Gollamudi, Owen Arden, and Stephen Chong. 6/2019. “Information Flow Control for Distributed Trusted Execution Environments.” In Computer Security Foundations. Publisher's VersionAbstract
Distributed applications cannot assume that their security policies will be enforced on untrusted hosts. Trusted execution environments (TEEs) combined with cryptographic mechanisms enable execution of known code on an untrusted host and the exchange of confidential and authenticated messages with it. TEEs do not, however, establish the trustworthiness of code executing in a TEE. Thus, developing secure applications using TEEs requires specialized expertise and careful auditing. This paper presents DFLATE, a core security calculus for distributed applications with TEEs. DFLATE offers high-level abstractions that reflect both the guarantees and limitations of the underlying security mechanisms they are based on. The accuracy of these abstractions is exhibited by asymmetry between confidentiality and integrity in our formal results: DFLATE enforces a strong form of noninterference for confidentiality, but only a weak form for integrity. This reflects the asymmetry of the security guarantees of a TEE: a malicious host cannot access secrets in the TEE or modify its contents, but they can suppress or manipulate the sequence of its inputs and outputs. Therefore DFLATE cannot protect against the suppression of high-integrity messages, but when these messages are delivered, their contents cannot have been influenced by an attacker.
CSF 2019
Benny Applebaum, Amos Beimel, Oriol Farr`as, Oded Nir, and Naty Peter. 5/2019. “Secret-Sharing Schemes for General and Uniform Access Structures.” In Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2019). Springer VersionAbstract
A secret-sharing scheme allows some authorized sets of parties to reconstruct a secret; the collection of authorized sets is called the access structure. For over 30 years, it was known that any (monotone) collection of authorized sets can be realized by a secret-sharing scheme whose shares are of size2n−o(n)and until recently no better scheme was known. In a recent breakthrough, Liu and Vaikuntanathan (STOC 2018) have reduced the share size to O(20.994n) Our first contribution is improving the exponent of secret sharing down to 0.892. For the special case of linear secret-sharing schemes, we get an exponent of 0.942 (compared to 0.999 of Liu and Vaikuntanathan).Motivated by the construction of Liu and Vaikuntanathan, we study secret-sharing schemes for uniform access structures. An access structure is k-uniform if all sets of size larger than k are authorized, all sets of size smaller than k are unauthorized, and each set of size k can be either authorized or unauthorized. The construction of Liu and Vaikuntanathan starts from protocols for conditional disclosure of secrets, constructs secret-sharing schemes for uniform access structures from them, and combines these schemes in order to obtain secret-sharing schemes for general access structures. Our second contribution in this paper is constructions of secret-sharing schemes for uniform access structures. We achieve the following results:A secret-sharing scheme for k-uniform access structures for large secrets in which the share size is O(k2) times the size of the secret.A linear secret-sharing scheme for k-uniform access structures for a binary secret in which the share size is~O(2h(k/n)n/2) (where h is the binary entropy function). By counting arguments, this construction is optimal (up to polynomial factors).A secret-sharing scheme for k-uniform access structures for a binary secret in which the share size is 2~O(√klogn). Our third contribution is a construction of ad-hoc PSM protocols, i.e., PSM protocols in which only a subset of the parties will compute a function on their inputs. This result is based on ideas we used in the construction of secret-sharing schemes for k-uniform access structures for a binary secret.
EPRINT 2019.pdf
Kobbi Nissim and Uri Stemmer. 3/2019. “Concentration Bounds for High Sensitivity Functions Through Differential Privacy.” Journal of Privacy and Confidentiality, 9, 1. Publisher's 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.

JPC.PDF
Amos Beimel, Shay Moran, Kobbi Nissim, and Uri Stemmer. 2/2019. “Private Center Points and Learning of Halfspaces.” In 32nd Annual Conference on Learning Theory, vol 99: Pp. 1–14. Publisher's VersionAbstract
We present a private learner for halfspaces over an arbitrary finite domain X⊂ℝd with sample complexity mathrmpoly(d,2log∗|X|). The building block for this learner is a differentially private algorithm for locating an approximate center point of m>poly(d,2log∗|X|) points -- a high dimensional generalization of the median function. Our construction establishes a relationship between these two problems that is reminiscent of the relation between the median and learning one-dimensional thresholds [Bun et al.\ FOCS '15]. This relationship suggests that the problem of privately locating a center point may have further applications in the design of differentially private algorithms.
We also provide a lower bound on the sample complexity for privately finding a point in the convex hull. For approximate differential privacy, we show a lower bound of m=Ω(d+log|X|), whereas for pure differential privacy m=Ω(d log|X|).
COLT 2019.pdf ARXIV 2019.PDF
2018
Kobbi Nissim and Uri Stemmer. 4/2018. “Clustering Algorithms for the Centralized and Local Models.” Conference on Algorithmic Learning Theory (ALT 2018).
Mitali Bafna, Jack Murtagh, and Nikhil Vyas. 2018. “Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform.” Advances in Neural Information Processing Systems, 31, Pp. 10096--10106. Publisher's Version PDF
2017
Shiva Kasiviswanathan, Kobbi Nissim, and Hongxia Jin. 2017. “Private Incremental Regression.” in the ACM SIGMOD/PODS Conference (PODS 2017).
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 . PDF
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. PDF
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.

PDF
G. Barthe, P.­Y. Strub, J. Hsu, A. D. Gordon, E. J. Gallego Arias, M. Gaboardi, and G. P. Farina. 2016. “Differentially Private Bayesian Programming.” 23rd ACM Conference on Computer and Communications Security, CCS. p68-barthe.pdf