4,229 research outputs found
Bounded-Leakage Differential Privacy
We introduce and study a relaxation of differential privacy [Dwork et al., 2006] that accounts for mechanisms that leak some additional, bounded information about the database. We apply this notion to reason about two distinct settings where the notion of differential privacy is of limited use. First, we consider cases, such as in the 2020 US Census [Abowd, 2018], in which some information about the database is released exactly or with small noise. Second, we consider the accumulation of privacy harms for an individual across studies that may not even include the data of this individual. The tools that we develop for bounded-leakage differential privacy allow us reason about privacy loss in these settings, and to show that individuals preserve some meaningful protections
Universal Privacy Gurantees for Smart Meters
Smart meters (SMs) provide advanced monitoring of consumer energy usage, thereby enabling optimized management and control of electricity distribution systems. Unfortunately, the data collected by SMs can reveal information about consumer activity, such as the times at which they run individual appliances. Two approaches have been proposed to tackle the privacy threat posed by such information leakage. One strategy involves manipulating user data before sending it to the utility provider (UP); this approach improves privacy at the cost of reducing the operational insight provided by the SM data to the UP. The alternative strategy employs rechargeable batteries or local energy sources at each consumer site to try decouple energy usage from energy requests. This thesis investigates the latter approach.
Understanding the privacy implications of any strategy requires an appropriate privacy metric.
A variety of metrics are used to study privacy in energy distribution systems. These include statistical distance metrics, differential privacy, distortion metrics, maximal leakage, maximal -leakage and information measures like mutual information. We here use mutual information to measure privacy both because its well understood fundamental properties and because it provides a useful bridge to adjacent fields such as hypothesis testing, estimation, and statistical or machine learning.
Privacy leakage under mutual information measures has been studied under a variety of assumptions on the energy consumption of the user with a strong focus on i.i.d. and some exploration of markov processes. Since user energy consumption may be non-stationary, here we seek privacy guarantees that apply for general random process models of energy consumption. Moreover, we impose finite capacity bounds on batteries and include the price of the energy requested from the grid, thus minimizing the information leakage subject to a bound on the resulting energy bill. To that aim we model the energy management unit (EMU) as a deterministic finite-state channel, and adapt the Ahlswede-Kaspi coding strategy proposed for permuting channels to the SM privacy setting.
Within this setting, we derive battery policies providing privacy guarantees that hold for any bounded process modelling the energy consumption of the user, including non-ergodic and non-stationary processes. These guarantees are also presented for bounded processes with a known expected average consumption. The optimality of the battery policy is characterized by presenting the probability law of a random process that is tight with respect to the upper bound. Moreover, we derive single letter bounds characterizing the privacy-cost trade off in the presence of variable market price. Finally it is shown that the provided results hold for mutual information, maximal leakage, maximal-alpha leakage and the Arimoto and Sibson channel capacity
Differential Privacy: on the trade-off between Utility and Information Leakage
Differential privacy is a notion of privacy that has become very popular in
the database community. Roughly, the idea is that a randomized query mechanism
provides sufficient privacy protection if the ratio between the probabilities
that two adjacent datasets give the same answer is bound by e^epsilon. In the
field of information flow there is a similar concern for controlling
information leakage, i.e. limiting the possibility of inferring the secret
information from the observables. In recent years, researchers have proposed to
quantify the leakage in terms of R\'enyi min mutual information, a notion
strictly related to the Bayes risk. In this paper, we show how to model the
query system in terms of an information-theoretic channel, and we compare the
notion of differential privacy with that of mutual information. We show that
differential privacy implies a bound on the mutual information (but not
vice-versa). Furthermore, we show that our bound is tight. Then, we consider
the utility of the randomization mechanism, which represents how close the
randomized answers are, in average, to the real ones. We show that the notion
of differential privacy implies a bound on utility, also tight, and we propose
a method that under certain conditions builds an optimal randomization
mechanism, i.e. a mechanism which provides the best utility while guaranteeing
differential privacy.Comment: 30 pages; HAL repositor
Quantifying Differential Privacy under Temporal Correlations
Differential Privacy (DP) has received increased attention as a rigorous
privacy framework. Existing studies employ traditional DP mechanisms (e.g., the
Laplace mechanism) as primitives, which assume that the data are independent,
or that adversaries do not have knowledge of the data correlations. However,
continuously generated data in the real world tend to be temporally correlated,
and such correlations can be acquired by adversaries. In this paper, we
investigate the potential privacy loss of a traditional DP mechanism under
temporal correlations in the context of continuous data release. First, we
model the temporal correlations using Markov model and analyze the privacy
leakage of a DP mechanism when adversaries have knowledge of such temporal
correlations. Our analysis reveals that the privacy leakage of a DP mechanism
may accumulate and increase over time. We call it temporal privacy leakage.
Second, to measure such privacy leakage, we design an efficient algorithm for
calculating it in polynomial time. Although the temporal privacy leakage may
increase over time, we also show that its supremum may exist in some cases.
Third, to bound the privacy loss, we propose mechanisms that convert any
existing DP mechanism into one against temporal privacy leakage. Experiments
with synthetic data confirm that our approach is efficient and effective.Comment: appears at ICDE 201
Differential Privacy versus Quantitative Information Flow
Differential privacy is a notion of privacy that has become very popular in
the database community. Roughly, the idea is that a randomized query mechanism
provides sufficient privacy protection if the ratio between the probabilities
of two different entries to originate a certain answer is bound by e^\epsilon.
In the fields of anonymity and information flow there is a similar concern for
controlling information leakage, i.e. limiting the possibility of inferring the
secret information from the observables. In recent years, researchers have
proposed to quantify the leakage in terms of the information-theoretic notion
of mutual information. There are two main approaches that fall in this
category: One based on Shannon entropy, and one based on R\'enyi's min entropy.
The latter has connection with the so-called Bayes risk, which expresses the
probability of guessing the secret. In this paper, we show how to model the
query system in terms of an information-theoretic channel, and we compare the
notion of differential privacy with that of mutual information. We show that
the notion of differential privacy is strictly stronger, in the sense that it
implies a bound on the mutual information, but not viceversa
Formal Verification of Differential Privacy for Interactive Systems
Differential privacy is a promising approach to privacy preserving data
analysis with a well-developed theory for functions. Despite recent work on
implementing systems that aim to provide differential privacy, the problem of
formally verifying that these systems have differential privacy has not been
adequately addressed. This paper presents the first results towards automated
verification of source code for differentially private interactive systems. We
develop a formal probabilistic automaton model of differential privacy for
systems by adapting prior work on differential privacy for functions. The main
technical result of the paper is a sound proof technique based on a form of
probabilistic bisimulation relation for proving that a system modeled as a
probabilistic automaton satisfies differential privacy. The novelty lies in the
way we track quantitative privacy leakage bounds using a relation family
instead of a single relation. We illustrate the proof technique on a
representative automaton motivated by PINQ, an implemented system that is
intended to provide differential privacy. To make our proof technique easier to
apply to realistic systems, we prove a form of refinement theorem and apply it
to show that a refinement of the abstract PINQ automaton also satisfies our
differential privacy definition. Finally, we begin the process of automating
our proof technique by providing an algorithm for mechanically checking a
restricted class of relations from the proof technique.Comment: 65 pages with 1 figur
- …