R. Bassily, Smith, A., and Thakurta, A., “

Private Empirical Risk Minimization, Revisited

,” in ICML 2014 Workshop on Learning, Security and Privacy, , Beijing, China, 2014. Publisher's VersionAbstract
In this paper, we initiate a systematic investigation of differentially private algorithms for convex empirical risk minimization. Various instantiations of this problem have been studied before. We provide new algorithms and matching lower bounds for private ERM assuming only that each data point's contribution to the loss function is Lipschitz bounded and that the domain of optimization is bounded. We provide a separate set of algorithms and matching lower bounds for the setting in which the loss functions are known to also be strongly convex.  Our algorithms run in polynomial time, and in some cases even match the optimal non-private running time (as measured by oracle complexity). We give separate algorithms (and lower bounds) for (ϵ,0)- and (ϵ,δ)-differential privacy; perhaps surprisingly, the techniques used for designing optimal algorithms in the two cases are completely different. Our lower bounds apply even to very simple, smooth function families, such as linear and quadratic functions. This implies that algorithms from previous work can be used to obtain optimal error rates, under the additional assumption that the contributions of each data point to the loss function is smooth. We show that simple approaches to smoothing arbitrary loss functions (in order to apply previous techniques) do not yield optimal error rates. In particular, optimal algorithms were not previously known for problems such as training support vector machines and the high-dimensional median.
A. Wood, et al.,

Integrating Approaches to Privacy Across the Research Lifecycle: Long-Term Longitudinal Studies

. Cambridge: Harvard University, 2014. Publisher's VersionAbstract
On September 24-25, 2013, the Privacy Tools for Sharing Research Data project at Harvard University held a workshop titled "Integrating Approaches to Privacy across the Research Data Lifecycle." Over forty leading experts in computer science, statistics, law, policy, and social science research convened to discuss the state of the art in data privacy research. The resulting conversations centered on the emerging tools and approaches from the participants’ various disciplines and how they should be integrated in the context of real-world use cases that involve the management of confidential research data.This workshop report, the first in a series, provides an overview of the long-term longitudinal study use case. Long-term longitudinal studies collect, at multiple points over a long period of time, highly-specific and often sensitive data describing the health, socioeconomic, or behavioral characteristics of human subjects. The value of such studies lies in part in their ability to link a set of behaviors and changes to each individual, but these factors tend to make the combination of observable characteristics associated with each subject unique and potentially identifiable.Using the research information lifecycle as a framework, this report discusses the defining features of long-term longitudinal studies and the associated challenges for researchers tasked with collecting and analyzing such data while protecting the privacy of human subjects. It also describes the disclosure risks and common legal and technical approaches currently used to manage confidentiality in longitudinal data. Finally, it identifies urgent problems and areas for future research to advance the integration of various methods for preserving confidentiality in research data.
C. Dimoulas, Moore, S., Askarov, A., and Chong, S., “

Declarative Policies for Capability Control

,” in Proceedings of the 27th {IEEE} Computer Security Foundations Symposium, , Piscataway, NJ, USA, 2014.Abstract
In capability-safe languages, components can access a resource only if they possess a capability for that resource. As a result, a programmer can prevent an untrusted component from accessing a sensitive resource by ensuring that the component never acquires the corresponding capability. In order to reason about which components may use a sensitive resource it is necessary to reason about how capabilities propagate through a system. This may be difficult, or, in the case of dynamically composed code, impossible to do before running the system. To counter this situation, we propose extensions to capability-safe languages that restrict the use of capabilities according to declarative policies. We introduce two independently useful semantic security policies to regulate capabilities and describe language-based mechanisms that enforce them. Access control policies restrict which components may use a capability and are enforced using higher-order contracts. Integrity policies restrict which components may influence (directly or indirectly) the use of a capability and are enforced using an information-flow type system. Finally, we describe how programmers can dynamically and soundly combine components that enforce access control or integrity policies with components that enforce different policies or even no policy at all.
M. Kearns, Pai, M., Roth, A., and Ullman, J., “

Mechanism Design in Large Games: Incentives and Privacy

,” in Proceedings of the 5th Conference on Innovations in Theoretical Computer Science, , New York, NY, USA, 2014, pp. 403–410. Publisher's Version p403-kearns.pdf
K. Chandrasekaran, Thaler, J., Ullman, J., and Wan, A., “

Faster Private Release of Marginals on Small Databases

,” in Proceedings of the 5th Conference on Innovations in Theoretical Computer Science, , New York, NY, USA, 2014, pp. 387–402. Publisher's Version p387-chandrasekaran.pdf
M. Bun, Ullman, J., and Vadhan, S., “

Fingerprinting Codes and the Price of Approximate Differential Privacy

,” in Proceedings of the 46th Annual ACM Symposium on Theory of Computing, , New York, NY, USA, 2014, pp. 1–10. Publisher's Version p1-bun.pdf
K. Nissim, Vadhan, S., and Xiao, D., “

Redrawing the Boundaries on Purchasing Data from Privacy-sensitive Individuals

,” in Proceedings of the 5th Conference on Innovations in Theoretical Computer Science, , New York, NY, USA, 2014, pp. 411–422. Publisher's Version p411-nissim.pdf
V. Feldman and Xiao, D., “

Sample Complexity Bounds on Differentially Private Learning via Communication Complexity

,” Proceedings of The 27th Conference on Learning Theory (COLT 2014), , vol. 35. JMLR Workshop and Conference Proceedings, pp. 1-20, 2014. Publisher's VersionAbstract
In this work we analyze the sample complexity of classification by differentially private algorithms. Differential privacy is a strong and well-studied notion of privacy introduced by Dwork et al. (2006) that ensures that the output of an algorithm leaks little information about the data point provided by any of the participating individuals. Sample complexity of private PAC and agnostic learning was studied in a number of prior works starting with (Kasiviswanathan et al., 2008) but a number of basic questions still remain open (Beimel et al. 2010; Chaudhuri and Hsu, 2011; Beimel et al., 2013ab).  Our main contribution is an equivalence between the sample complexity of differentially-private learning of a concept class C (or SCDP(C)) and the randomized one-way communication complexity of the evaluation problem for concepts from C. Using this equivalence we prove the following bounds:
  • SCDP(C)=Ω(LDim(C)), where LDim(C) is the Littlestone's (1987) dimension characterizing the number of mistakes in the online-mistake-bound learning model. This result implies that SCDP(C) is different from the VC-dimension of C, resolving one of the main open questions from prior work.
  • For any t, there exists a class C such that LDim(C)=2 but SCDP(C)≥t.
  • For any t, there exists a class C such that the sample complexity of (pure) α-differentially private PAC learning is Ω(t/α) but the sample complexity of the relaxed (α,β)-differentially private PAC learning is O(log(1/β)/α). This resolves an open problem from (Beimel et al., 2013b). 
We also obtain simpler proofs for a number of known related results. Our equivalence builds on a characterization of sample complexity by Beimel et al., (2013a) and our bounds rely on a number of known results from communication complexity.
J. Hsu, et al., “

Privately Solving Linear Programs

,” in Automata, Languages, and Programming, , vol. 8572, Springer Berlin Heidelberg, 2014, pp. 612-624. Publisher's Version 1402.3631v2.pdf
M. M. Pai, Roth, A., and Ullman, J., “

An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring

,” CoRR, , vol. abs/1402.2801, 2014. 1402.2801v1.pdf
D. J. Weitzner, et al., “

Consumer Privacy Bill of Rights and Big Data: Response to White House Office of Science and Technology Policy Request for Information

”. 2014.Abstract
In response to the White House Office of Science and Technology Policy Request for Information on Big Data Privacy we offer these comments based on presentations and discussions at the White House-MIT Workshop “Big Data Privacy Workshop: Advancing the State of the Art in Technology and Practice” and subsequent workshops co-sponsored with Data & Society and NYU Information Law Institute and the UC Berkeley iSchool.
PDF version
M. Altman, O’Brien, D., and Wood, A., “

Comment on the Occupational Safety and Health Administration (OSHA) Proposed Rule: Improve Tracking of Workplace Injuries and Illnesses; Extension of Comment Period

”. 2014. Full Text at PDF version of comments
S. H. Chan, Costa, T. B., and Airoldi, E. M., “

Estimation of exchangeable graph models by stochastic blockmodel approximation

,” in Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, , 2013, pp. 293-296. chan_costa_airoldi_2013.pdf
S. Hooley and Sweeney, L., “Survey of Publicly Available State Health Databases,” Data Privacy Lab, IQSS, Harvard University, . 2013. Project website PDF
L. Sweeney, “Matching Known Patients to Health Records in Washington State Data,” Data Privacy Lab, IQSS, Harvard University, . 2013. Project website PDF
L. Sweeney, Abu, A., and Winn, J., “Identifying Participants in the Personal Genome Project by Name,” Data Privacy Lab, IQSS, Harvard University, . 2013. Project website PDF
S. P. Kasiviswanathan, Nissim, K., Raskhodnikova, S., and Smith, A., “

Analyzing Graphs with Node Differential Privacy

,” in Proceedings of the 10th Theory of Cryptography Conference on Theory of Cryptography, , Berlin, Heidelberg, 2013, pp. 457–476. Publisher's Version chp3a10.10072f978-3-642-36594-2_26.pdf
A. Beimel, Nissim, K., and Stemmer, U., “

Characterizing the Sample Complexity of Private Learners

,” in Proceedings of the 4th Conference on Innovations in Theoretical Computer Science, , New York, NY, USA, 2013, pp. 97–110. Publisher's Version p97-beimel_1.pdf
J. Ullman, “Answering n{2+o(1)} counting queries with differential privacy is hard,” in Proceedings of the 45th annual ACM symposium on Symposium on theory of computing, , Palo Alto, California, USA, 2013, pp. 361-370. DOIAbstract
A central problem in differentially private data analysis is how to design efficient algorithms capable of answering large numbers of counting queries on a sensitive database. Counting queries are of the form "What fraction of individual records in the database satisfy the property q?" We prove that if one-way functions exist, then there is no algorithm that takes as input a database db ∈ dbset, and k = ~Θ(n2) arbitrary efficiently computable counting queries, runs in time poly(d, n), and returns an approximate answer to each query, while satisfying differential privacy. We also consider the complexity of answering "simple" counting queries, and make some progress in this direction by showing that the above result holds even when we require that the queries are computable by constant-depth (AC0) circuits. Our result is almost tight because it is known that ~Ω(n2) counting queries can be answered efficiently while satisfying differential privacy. Moreover, many more than n2 queries (even exponential in n) can be answered in exponential time. We prove our results by extending the connection between differentially private query release and cryptographic traitor-tracing schemes to the setting where the queries are given to the sanitizer as input, and by constructing a traitor-tracing scheme that is secure in this setting.
J. Hsu, Roth, A., and Ullman, J., “Differential privacy for the analyst via private equilibrium computation,” in Proceedings of the 45th annual ACM symposium on Symposium on theory of computing, , Palo Alto, California, USA, 2013, pp. 341-350. DOIAbstract
We give new mechanisms for answering exponentially many queries from multiple analysts on a private database, while protecting dif- ferential privacy both for the individuals in the database and for the analysts. That is, our mechanism's answer to each query is nearly insensitive to changes in the queries asked by other analysts. Our mechanism is the first to offer differential privacy on the joint distribution over analysts' answers, providing privacy for data an- alysts even if the other data analysts collude or register multiple accounts. In some settings, we are able to achieve nearly optimal error rates (even compared to mechanisms which do not offer an- alyst privacy), and we are able to extend our techniques to handle non-linear queries. Our analysis is based on a novel view of the pri- vate query-release problem as a two-player zero-sum game, which may be of independent interest.