Publications by Year: 2015

2015
Kobbi Nissim and David Xiao. 2015. “Mechanism Design and Differential Privacy.” In Encyclopedia of Algorithms, Pp. 1-12. New York, NY: Springer Berlin Heidelberg. Publisher's Version
Latanya Sweeney and Merce Crosas. 2015. “An Open Science Platform for the Next Generation of Data.” Arxiv.org Computer Science, Computers and Scoiety [Internet]. ArXiv VersionAbstract

Imagine an online work environment where researchers have direct and immediate access to myriad data sources and tools and data management resources, useful throughout the research lifecycle. This is our vision for the next generation of the Dataverse Network: an Open Science Platform (OSP). For the first time, researchers would be able to seamlessly access and create primary and derived data from a variety of sources: prior research results, public data sets, harvested online data, physical instruments, private data collections, and even data from other standalone repositories. Researchers could recruit research participants and conduct research directly on the OSP, if desired, using readily available tools. Researchers could create private or shared workspaces to house data, access tools, and computation and could publish data directly on the platform or publish elsewhere with persistent, data citations on the OSP. This manuscript describes the details of an Open Science Platform and its construction. Having an Open Science Platform will especially impact the rate of new scientific discoveries and make scientific findings more credible and accountable.

PDF
Latanya Sweeney. 2015. “Privacy as a Sword and Shield in Public Health.” New York City Department of Public Health. New York, NY. .
Micah Altman. 2015. “Privacy Principles (framing talk).” United Nations Global Pulse Workshop on ICT4D Principle 8: Address Privacy & Security In Development Programs. New York, USA.
Or Sheffet. 2015. “Private Approximations of the 2nd-Moment Matrix Using Existing Techniques in Linear Regression”. ArXiv VersionAbstract

We introduce three differentially-private algorithms that approximates the 2nd-moment matrix of the data. These algorithm, which in contrast to existing algorithms output positive-definite matrices, correspond to existing techniques in linear regression literature. Specifically, we discuss the following three techniques. (i) For Ridge Regression, we propose setting the regularization coefficient so that by approximating the solution using Johnson-Lindenstrauss transform we preserve privacy. (ii) We show that adding a small batch of random samples to our data preserves differential privacy. (iii) We show that sampling the 2nd-moment matrix from a Bayesian posterior inverse-Wishart distribution is differentially private provided the prior is set correctly. We also evaluate our techniques experimentally and compare them to the existing "Analyze Gauss" algorithm of Dwork et al.

C. Dwork, A Smith, T Steinke, J Ullman, and S. Vadhan. 2015. “Robust Traceability from Trace Amounts.” In IEEE Symposium on Foundations of Computer Science (FOCS 2015). Berkeley, California.Abstract

The privacy risks inherent in the release of a large number of summary statistics were illustrated by Homer et al. (PLoS Genetics, 2008), who considered the case of 1-way marginals of SNP allele frequencies obtained in a genome-wide association study: Given a large number of minor allele frequencies from a case group of individuals diagnosed with a particular disease, together with the genomic data of a single target individual and statistics from a sizable reference dataset independently drawn from the same population, an attacker can determine with high confidence whether or not the target is in the case group. In this work we describe and analyze a simple attack that succeeds even if the summary statistics are significantly distorted, whether due to measurement error or noise intentionally introduced to protect privacy. Our attack only requires that the vector of distorted summary statistics is close to the vector of true marginals in `1 norm. Moreover, the reference pool required by previous attacks can be replaced by a single sample drawn from the underlying population. The new attack, which is not specific to genomics and which handles Gaussian as well as Bernouilli data, significantly generalizes recent lower bounds on the noise needed to ensure differential privacy (Bun, Ullman, and Vadhan, STOC 2014; Steinke and Ullman, 2015), obviating the need for the attacker to control the exact distribution of the data.

PDF
Latanya Sweeney, Mercè Crosas, and Michael Bar-Sinai. 2015. “Sharing Sensitive Data with Confidence: The Datatags System.” Technology Science . Online VersionAbstract

Society generates data on a scale previously unimagined. Wide sharing of these data promises to improve personal health, lower healthcare costs, and provide a better quality of life. There is a tendency to want to share data freely. However, these same data often include sensitive information about people that could cause serious harms if shared widely. A multitude of regulations, laws and best practices protect data that contain sensitive personal information. Government agencies, research labs, and corporations that share data, as well as review boards and privacy officers making data sharing decisions, are vigilant but uncertain. This uncertainty creates a tendency not to share data at all. Some data are more harmful than other data; sharing should not be an all-or-nothing choice. How do we share data in ways that ensure access is commensurate with risks of harm?

Mark Bun, Kobbi Nissim, and Uri Stemmer. 2015. “Simultaneous private learning of multiple concepts.” In .Abstract

We investigate the direct-sum problem in the context of differentially private PAC learning: What is the sample complexity of solving k learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving k learning tasks without privacy? In our setting, an individual example consists of a domain element x labeled by k unknown concepts (c1,,ck). The goal of a multi-learner is to output k hypotheses (h1,,hk) that generalize the input examples. 
Without concern for privacy, the sample complexity needed to simultaneously learn k concepts is essentially the same as needed for learning a single concept. Under differential privacy, the basic strategy of learning each hypothesis independently yields sample complexity that grows polynomially with k. For some concept classes, we give multi-learners that require fewer samples than the basic strategy. Unfortunately, however, we also give lower bounds showing that even for very simple concept classes, the sample cost of private multi-learning must grow polynomially in k.

PDF
L Cranor, T Rabin, V Shmatikov, S Vadhan, and D Weitzner. 2015. “Towards a Privacy Research Roadmap for the Computing Community..” Report for the Computing Community Consortium (CCC). CCC Version
David Abrams. 2015. “What Stays in Vegas: The Road to “Zero Privacy".” New England Law Review, 49, 4. Publisher's Version

Pages