Webinar: https://www.youtube.com/watch?v=cOu-sTV8J2M

This chapter explains how administrative data containing personal information can be collected, analyzed, and published in a way that ensures the individuals in the data will be afforded the strong protections of differential privacy.

It is intended as a practical resource for government agencies and research organizations interested in exploring the possibility of implementing tools for differentially private data sharing and analysis. Using intuitive examples rather than the mathematical formalism used in other guides, this chapter introduces the differential privacy definition and the risks it was developed to address. The text employs modern privacy frameworks to explain how to determine whether the use of differential privacy is an appropriate solution in a given setting. It also discusses the design considerations one should take into account when implementing differential privacy. This discussion incorporates a review of real-world implementations, including tools designed for tiered access systems combining differential privacy with other disclosure controls presented in this Handbook, such as consent mechanisms, data use agreements, and secure environments.

Differential privacy technology has passed a preliminary transition from being the subject of academic work to initial implementations by large organizations and high-tech companies that have the expertise to develop and implement customized differentially private methods. With a growing collection of software packages for generating differentially private releases from summary statistics to machine learning models, differential privacy is now transitioning to being usable more widely and by smaller organizations.

Version History: Original version released as a Working Paper for the May 2020 OpenDP Community Meeting (version attached as MAY 2020.pdf, and accessible online at https://projects.iq.harvard.edu/files/opendp/files/opendp_programming_fr...). 

Talks: View a talk on this paper presented by Marco Gaboardi and Michael Hay at the 2020 OpenDP Community Meeting. 

Subsequently presented as a poster at TPDP 2020 (attached as TPDP2020.pdf). 

In this working paper, we propose a programming framework for the library of differentially private algorithms that will be at the core of the OpenDP open-source software project, and recommend programming languages in which to implement the framework.

• View a talk on this paper presented at the 2020 OpenDP Community Meeting
• View a talk on this paper presented at TPDP 2020

OpenDP is led by Faculty Directors Gary King and Salil Vadhan and an Executive Committee at Harvard University, funded in part by a grant from the Sloan Foundation. Its efforts so far have included implementing a differentially private curator application in collaboration with Microsoft, and developing a framework for a community-driven OpenDP Commons through the work of an Ad Hoc Design Committee including external experts. Going forward, the project plans to engage with a wide community of stakeholders, establish partnerships with a wide variety of groups from industry, academia, and government, and adopt increasing levels of community governance.

Version History: Full version of part of an STOC 2009 paper.

We put forth a new computational notion of entropy, measuring the (in)feasibility of sampling high-entropy strings that are consistent with a given generator. Specifically, the \(i\)'th output block of a generator \(\mathsf{G}\) has accessible entropy at most \(k\) if the following holds: when conditioning on its prior coin tosses, no polynomial-time strategy \(\mathsf{\widetilde{G}}\) can generate valid output for \(\mathsf{G}\)'s \(i \)'th output block with entropy greater than \(k\). A generator has inaccessible entropy if the total accessible entropy (summed over the blocks) is noticeably smaller than the real entropy of \(\mathsf{G}\)'s output.

As an application of the above notion, we improve upon the result of Haitner, Nguyen, Ong, Reingold, and Vadhan [Sicomp '09], presenting a much simpler and more efficient construction of statistically hiding commitment schemes from arbitrary one-way functions.

Version History: published earlier in Henri Gilbert, ed., Advances in Cryptology—EUROCRYPT ‘10, Lecture Notes on Computer Science, as "Universal one-way hash functions via inaccessible entropy": 

https://link.springer.com/chapter/10.1007/978-3-642-13190-5_31

This paper revisits the construction of Universal One-Way Hash Functions (UOWHFs) from any one-way function due to Rompel (STOC 1990). We give a simpler construction of UOWHFs, which also obtains better efficiency and security. The construction exploits a strong connection to the recently introduced notion of inaccessible entropy (Haitner et al. STOC 2009). With this perspective, we observe that a small tweak of any one-way function \(f\) is already a weak form of a UOWHF: Consider \(F(x', i)\) that outputs the \(i\)-bit long prefix of \(f(x)\). If \(F\) were a UOWHF then given a random \(x\) and \(i\) it would be hard to come up with \(x' \neq x\) such that \(F(x, i) = F(x', i)\). While this may not be the case, we show (rather easily) that it is hard to sample \(x'\) with almost full entropy among all the possible such values of \(x'\). The rest of our construction simply amplifies and exploits this basic property.

With this and other recent works, we have that the constructions of three fundamental cryptographic primitives (Pseudorandom Generators, Statistically Hiding Commitments and UOWHFs) out of one-way functions are to a large extent unified. In particular, all three constructions rely on and manipulate computational notions of entropy in similar ways. Pseudorandom Generators rely on the well-established notion of pseudoentropy, whereas Statistically Hiding Commitments and UOWHFs rely on the newer notion of inaccessible entropy.

Version History: Full version posted as ECCC TR10-017.

Published earlier in Yuval Ishai, ed., Proceedings of the 8th IACR Theory of Cryptography Conference (TCC ‘11), Lecture Notes on Computer Science. Springer-Verlag, Publishers: Vol. 5978, pp. 572-587. https://link.springer.com/chapter/10.1007/978-3-642-19571-6_24

Invited to J. Cryptology selected papers from TCC 2011.

Assuming the existence of one-way functions, we show that there is no polynomial-time, differentially private algorithm \(\mathcal{A}\) that takes a database \(D ∈ ({0, 1}^d)^n\) and outputs a “synthetic database” \(\hat{D}\) all of whose two-way marginals are approximately equal to those of \(D\). (A two-way marginal is the fraction of database rows \(x ∈ {0, 1}^d\) with a given pair of values in a given pair of columns.) This answers a question of Barak et al. (PODS ‘07), who gave an algorithm running in time \(\mathrm{poly} (n, 2^d)\).

Our proof combines a construction of hard-to-sanitize databases based on digital signatures (by Dwork et al., STOC ‘09) with encodings based on probabilistically checkable proofs.

We also present both negative and positive results for generating “relaxed” synthetic data, where the fraction of rows in \(D\) satisfying a predicate \(c\) are estimated by applying \(c\) to each row of \(\hat{D}\) and aggregating the results in some way.

Version History: 

Previously published as: Yiling Chen, Or Sheffet, and Salil Vadhan. Privacy games. In Proceedings of the 10th International Conference on Web and Internet Economics (WINE ‘14), volume 8877 of Lecture Notes in Computer Science, pages 371–385. Springer-Verlag, 14–17 December 2014. (WINE Publisher's Version linked here: https://link.springer.com/chapter/10.1007/978-3-319-13129-0_30); PDF attached as WINE2014.

The problem of analyzing the effect of privacy concerns on the behavior of selfish utility-maximizing agents has received much attention lately. Privacy concerns are often modeled by altering the utility functions of agents to consider also their privacy loss. Such privacy aware agents prefer to take a randomized strategy even in very simple games in which non-privacy aware agents play pure strategies. In some cases, the behavior of privacy aware agents follows the framework of Randomized Response, a well-known mechanism that preserves differential privacy. 

Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent A, in an attempt to discover the secret type of agent B, offers B a gift that one type of B agent likes and the other type dislikes. As opposed to previous works, B's incentive to keep her type a secret isn't the result of "hardwiring" B's utility function to consider privacy, but rather takes the form of a payment between B and A. We investigate three different types of payment functions and analyze B's behavior in each of the resulting games. As we show, under some payments, B's behavior is very different than the behavior of agents with hardwired privacy concerns and might even be deterministic. Under a different payment we show that B's BNE strategy does fall into the framework of Randomized Response.