Publications by Year: 2020

2020
Marco Gaboardi, Kobbi Nissim, and David Purser. 7/2020. “The Complexity of Verifying Loop-free Programs as Differentially Private.” In 47th International Colloquium on Automata, Languages and Programming (To appear - ICALP 2020). ArXiv VersionAbstract

We study the problem of verifying differential privacy for loop-free programs with probabilistic choice. Programs in this class can be seen as randomized Boolean circuits, which we will use as a formal model to answer two different questions: first, deciding whether a program satisfies a prescribed level of privacy; second, approximating the privacy parameters a program realizes. We show that the problem of deciding whether a program satisfies "-differential privacy is coNP#P-complete. In fact, this is the case when either the input domain or the output range of the program is large.

Further, we show that deciding whether a program is (", )-differentially private is coNP#P-hard, and in coNP#P for small output domains, but always in coNP#P#P. Finally, we show that the problem of approximating the level of differential privacy is both NP-hard and coNP-hard. These results complement previous results by Murtagh and Vadhan [34] showing that deciding the optimal composition of differentially private components is #P-complete, and that approximating the optimal composition of differentially private components is in P.

ARXIV.pdf
Micah Altman, Stephen Chong, and Alexandra Wood. 7/2020. “Formalizing Privacy Laws for License Generation and Data Repository Decision Automation.” In 20th Privacy Enhancing Technologies Symposium (To appear - PET 2020). ArXiv VersionAbstract
In this paper, we summarize work-in-progress on expert system support to automate some data deposit and release decisions within a data repository, and to generate custom license agreements for those data transfers.
Our approach formalizes via a logic programming language the privacy-relevant aspects of laws, regulations, and best practices, supported by legal analysis documented in legal memoranda. This formalization enables automated reasoning about the conditions under which a repository can transfer data, through interrogation of users, and the application of formal rules to the facts obtained from users. The proposed system takes the specific conditions for a given data release and produces a custom data use agreement that accurately captures the relevant restrictions on data use. This enables appropriate decisions and accurate licenses, while removing the bottleneck of lawyer effort per data transfer. The operation of the system aims to be transparent, in the sense that administrators, lawyers, institutional review boards, and other interested parties can evaluate the legal reasoning and interpretation embodied in the formalization, and the specific rationale for a decision to accept or release a particular dataset.
ARXIV 2019.pdf
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