Applying Theoretical Advances in Privacy to Computational Social Science Practice: Publications

Kobbi Nissim, Uri Stemmer, and Salil Vadhan. 2016. “Locating a Small Cluster Privately.” In PODS 2016. ACM SIGMOD/PODS Conference, San Francisco, USA, 2016.Abstract

We present a new algorithm for locating a small cluster of points with differential privacy [Dwork, McSherry, Nissim,and Smith, 2006]. Our algorithm has implications to private data exploration, clustering, and removal of outliers. Furthermore, we use it to significantly relax the requirements of the sample and aggregate technique [Nissim, Raskhodnikova,and Smith, 2007], which allows compiling of "off the shelf" (non-private) analyses into analyses that preserve differential privacy.

Micah Altman, Alexandra Wood, David R. O'Brien, and Urs Gasser. 2016. “Practical Approaches to Big Data Privacy Over Time.” Brussels Privacy Symposium.Abstract

Increasingly, governments and businesses are collecting, analyzing, and sharing detailed information about individuals over long periods of time. Vast quantities of data from new sources and novel methods for large-scale data analysis promise to yield deeper understanding of human characteristics, behavior, and relationships and advance the state of science, public policy, and innovation. At the same time, the collection and use of fine-grained personal data over time is associated with significant risks to individuals, groups, and society at large. In this article, we examine a range of longterm data collections, conducted by researchers in social science, in order to identify the characteristics of these programs that drive their unique sets of risks and benefits. We also examine the practices that have been established by social scientists to protect the privacy of data subjects in light of the challenges presented in long-term studies. We argue that many uses of big data, across academic, government, and industry settings, have characteristics similar to those of traditional long-term research studies. In this article, we discuss the lessons that can be learned from longstanding data management practices in research and potentially applied in the context of newly emerging data sources and uses.

Ryan Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan. 2016. “Privacy Odometers and Filters: Pay-as-you-Go Composition.” Neural Information Processing Systems (NIPS).Abstract

In this paper we initiate the study of adaptive composition in differential privacy when the length of the composition, and the privacy parameters themselves can be chosen adaptively, as a function of the outcome of previously run analyses. This case is much more delicate than the setting covered by existing composition theorems, in which the algorithms themselves can be chosen adaptively, but the privacy parameters must be fixed up front. Indeed, it isn't even clear how to define differential privacy in the adaptive parameter setting. We proceed by defining two objects which cover the two main use cases of composition theorems. A privacy filter is a stopping time rule that allows an analyst to halt a computation before his pre-specified privacy budget is exceeded. A privacy odometer allows the analyst to track realized privacy loss as he goes, without needing to pre-specify a privacy budget. We show that unlike the case in which privacy parameters are fixed, in the adaptive parameter setting, these two use cases are distinct. We show that there exist privacy filters with bounds comparable (up to constants) with existing privacy composition theorems. We also give a privacy odometer that nearly matches non-adaptive private composition theorems, but is sometimes worse by a small asymptotic factor. Moreover, we show that this is inherent, and that any valid privacy odometer in the adaptive parameter setting must lose this factor, which shows a formal separation between the filter and odometer use-cases.

Mercè Crosas, Gary King, James Honaker, and Latanya Sweeney. 2015. “Automating Open Science for Big Data.” The ANNALS of the American Academy of Political and Social Science, 659, 1, Pp. 260-273 . Publisher's VersionAbstract

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.