6,851 research outputs found
Privacy Games: Optimal User-Centric Data Obfuscation
In this paper, we design user-centric obfuscation mechanisms that impose the
minimum utility loss for guaranteeing user's privacy. We optimize utility
subject to a joint guarantee of differential privacy (indistinguishability) and
distortion privacy (inference error). This double shield of protection limits
the information leakage through obfuscation mechanism as well as the posterior
inference. We show that the privacy achieved through joint
differential-distortion mechanisms against optimal attacks is as large as the
maximum privacy that can be achieved by either of these mechanisms separately.
Their utility cost is also not larger than what either of the differential or
distortion mechanisms imposes. We model the optimization problem as a
leader-follower game between the designer of obfuscation mechanism and the
potential adversary, and design adaptive mechanisms that anticipate and protect
against optimal inference algorithms. Thus, the obfuscation mechanism is
optimal against any inference algorithm
Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy
Differential privacy is a promising privacy-preserving paradigm for
statistical query processing over sensitive data. It works by injecting random
noise into each query result, such that it is provably hard for the adversary
to infer the presence or absence of any individual record from the published
noisy results. The main objective in differentially private query processing is
to maximize the accuracy of the query results, while satisfying the privacy
guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an
appropriate strategy, processing a batch of correlated queries as a whole
achieves considerably higher accuracy than answering them individually.
However, to our knowledge there is currently no practical solution to find such
a strategy for an arbitrary query batch; existing methods either return
strategies of poor quality (often worse than naive methods) or require
prohibitively expensive computations for even moderately large domains.
Motivated by this, we propose low-rank mechanism (LRM), the first practical
differentially private technique for answering batch linear queries with high
accuracy. LRM works for both exact (i.e., -) and approximate (i.e.,
(, )-) differential privacy definitions. We derive the
utility guarantees of LRM, and provide guidance on how to set the privacy
parameters given the user's utility expectation. Extensive experiments using
real data demonstrate that our proposed method consistently outperforms
state-of-the-art query processing solutions under differential privacy, by
large margins.Comment: ACM Transactions on Database Systems (ACM TODS). arXiv admin note:
text overlap with arXiv:1212.230
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
Data-driven algorithm design, that is, choosing the best algorithm for a
specific application, is a crucial problem in modern data science.
Practitioners often optimize over a parameterized algorithm family, tuning
parameters based on problems from their domain. These procedures have
historically come with no guarantees, though a recent line of work studies
algorithm selection from a theoretical perspective. We advance the foundations
of this field in several directions: we analyze online algorithm selection,
where problems arrive one-by-one and the goal is to minimize regret, and
private algorithm selection, where the goal is to find good parameters over a
set of problems without revealing sensitive information contained therein. We
study important algorithm families, including SDP-rounding schemes for problems
formulated as integer quadratic programs, and greedy techniques for canonical
subset selection problems. In these cases, the algorithm's performance is a
volatile and piecewise Lipschitz function of its parameters, since tweaking the
parameters can completely change the algorithm's behavior. We give a sufficient
and general condition, dispersion, defining a family of piecewise Lipschitz
functions that can be optimized online and privately, which includes the
functions measuring the performance of the algorithms we study. Intuitively, a
set of piecewise Lipschitz functions is dispersed if no small region contains
many of the functions' discontinuities. We present general techniques for
online and private optimization of the sum of dispersed piecewise Lipschitz
functions. We improve over the best-known regret bounds for a variety of
problems, prove regret bounds for problems not previously studied, and give
matching lower bounds. We also give matching upper and lower bounds on the
utility loss due to privacy. Moreover, we uncover dispersion in auction design
and pricing problems
A Utility-Theoretic Approach to Privacy in Online Services
Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess usersâ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoplesâ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users
An Adaptive Mechanism for Accurate Query Answering under Differential Privacy
We propose a novel mechanism for answering sets of count- ing queries under
differential privacy. Given a workload of counting queries, the mechanism
automatically selects a different set of "strategy" queries to answer
privately, using those answers to derive answers to the workload. The main
algorithm proposed in this paper approximates the optimal strategy for any
workload of linear counting queries. With no cost to the privacy guarantee, the
mechanism improves significantly on prior approaches and achieves near-optimal
error for many workloads, when applied under (\epsilon, \delta)-differential
privacy. The result is an adaptive mechanism which can help users achieve good
utility without requiring that they reason carefully about the best formulation
of their task.Comment: VLDB2012. arXiv admin note: substantial text overlap with
arXiv:1103.136
p-probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation
We develop a probabilistic variant of k-anonymous microaggregation which we term p-probabilistic resorting to a statistical model of respondent participation in order to aggregate quasi-identifiers in such a manner that k-anonymity is concordantly enforced with a parametric probabilistic guarantee. Succinctly owing the possibility that some respondents may not finally participate, sufficiently larger cells are created striving to satisfy k-anonymity with probability at least p. The microaggregation function is designed before the respondents submit their confidential data. More precisely, a specification of the function is sent to them which they may verify and apply to their quasi-identifying demographic variables prior to submitting the microaggregated data along with the confidential attributes to an authorized repository.
We propose a number of metrics to assess the performance of our probabilistic approach in terms of anonymity and distortion which we proceed to investigate theoretically in depth and empirically with synthetic and standardized data. We stress that in addition to constituting a functional extension of traditional microaggregation, thereby broadening its applicability to the anonymization of statistical databases in a wide variety of contexts, the relaxation of trust assumptions is arguably expected to have a considerable impact on user acceptance and ultimately on data utility through mere availability.Peer ReviewedPostprint (author's final draft
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