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The Privacy of the Analyst and the Power of the State
We initiate the study of privacy for the analyst in differentially private data analysis. That is, not only will we be concerned with ensuring differential privacy for the data (i.e. individuals or customers), which are the usual concern of differential privacy, but we also consider (differential) privacy for the set of queries posed by each data analyst. The goal is to achieve privacy with respect to other analysts, or users of the system. This problem arises only in the context of stateful privacy mechanisms, in which the responses to queries depend on other queries posed (a recent wave of results in the area utilized cleverly coordinated noise and state in order to allow answering privately hugely many queries). We argue that the problem is real by proving an exponential gap between the number of queries that can be answered (with non-trivial error) by stateless and stateful differentially private mechanisms. We then give a stateful algorithm for differentially private data analysis that also ensures differential privacy for the analyst and can answer exponentially many queries.Engineering and Applied Science
The privacy of the analyst and the power of the state.
Abstract-We initiate the study of privacy for the analyst in differentially private data analysis. That is, not only will we be concerned with ensuring differential privacy for the data (i.e. individuals or customers), which are the usual concern of differential privacy, but we also consider (differential) privacy for the set of queries posed by each data analyst. The goal is to achieve privacy with respect to other analysts, or users of the system. This problem arises only in the context of stateful privacy mechanisms, in which the responses to queries depend on other queries posed (a recent wave of results in the area utilized cleverly coordinated noise and state in order to allow answering privately hugely many queries). We argue that the problem is real by proving an exponential gap between the number of queries that can be answered (with non-trivial error) by stateless and stateful differentially private mechanisms. We then give a stateful algorithm for differentially private data analysis that also ensures differential privacy for the analyst and can answer exponentially many queries
Privacy Tradeoffs in Predictive Analytics
Online services routinely mine user data to predict user preferences, make
recommendations, and place targeted ads. Recent research has demonstrated that
several private user attributes (such as political affiliation, sexual
orientation, and gender) can be inferred from such data. Can a
privacy-conscious user benefit from personalization while simultaneously
protecting her private attributes? We study this question in the context of a
rating prediction service based on matrix factorization. We construct a
protocol of interactions between the service and users that has remarkable
optimality properties: it is privacy-preserving, in that no inference algorithm
can succeed in inferring a user's private attribute with a probability better
than random guessing; it has maximal accuracy, in that no other
privacy-preserving protocol improves rating prediction; and, finally, it
involves a minimal disclosure, as the prediction accuracy strictly decreases
when the service reveals less information. We extensively evaluate our protocol
using several rating datasets, demonstrating that it successfully blocks the
inference of gender, age and political affiliation, while incurring less than
5% decrease in the accuracy of rating prediction.Comment: Extended version of the paper appearing in SIGMETRICS 201
Differential Privacy for the Analyst via Private Equilibrium Computation
We give new mechanisms for answering exponentially many queries from multiple
analysts on a private database, while protecting differential 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 analysts
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 analyst privacy), and we are able to extend our
techniques to handle non-linear queries. Our analysis is based on a novel view
of the private query-release problem as a two-player zero-sum game, which may
be of independent interest
The Future of the Internet III
Presents survey results on technology experts' predictions on the Internet's social, political, and economic impact as of 2020, including its effects on integrity and tolerance, intellectual property law, and the division between personal and work lives
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