96,621 research outputs found
Hypothesis Testing Interpretations and Renyi Differential Privacy
Differential privacy is a de facto standard in data privacy, with
applications in the public and private sectors. A way to explain differential
privacy, which is particularly appealing to statistician and social scientists
is by means of its statistical hypothesis testing interpretation. Informally,
one cannot effectively test whether a specific individual has contributed her
data by observing the output of a private mechanism---any test cannot have both
high significance and high power.
In this paper, we identify some conditions under which a privacy definition
given in terms of a statistical divergence satisfies a similar interpretation.
These conditions are useful to analyze the distinguishability power of
divergences and we use them to study the hypothesis testing interpretation of
some relaxations of differential privacy based on Renyi divergence. This
analysis also results in an improved conversion rule between these definitions
and differential privacy
Extremal Mechanisms for Local Differential Privacy
Local differential privacy has recently surfaced as a strong measure of
privacy in contexts where personal information remains private even from data
analysts. Working in a setting where both the data providers and data analysts
want to maximize the utility of statistical analyses performed on the released
data, we study the fundamental trade-off between local differential privacy and
utility. This trade-off is formulated as a constrained optimization problem:
maximize utility subject to local differential privacy constraints. We
introduce a combinatorial family of extremal privatization mechanisms, which we
call staircase mechanisms, and show that it contains the optimal privatization
mechanisms for a broad class of information theoretic utilities such as mutual
information and -divergences. We further prove that for any utility function
and any privacy level, solving the privacy-utility maximization problem is
equivalent to solving a finite-dimensional linear program, the outcome of which
is the optimal staircase mechanism. However, solving this linear program can be
computationally expensive since it has a number of variables that is
exponential in the size of the alphabet the data lives in. To account for this,
we show that two simple privatization mechanisms, the binary and randomized
response mechanisms, are universally optimal in the low and high privacy
regimes, and well approximate the intermediate regime.Comment: 52 pages, 10 figures in JMLR 201
"Pretty strong" converse for the private capacity of degraded quantum wiretap channels
In the vein of the recent "pretty strong" converse for the quantum and
private capacity of degradable quantum channels [Morgan/Winter, IEEE Trans.
Inf. Theory 60(1):317-333, 2014], we use the same techniques, in particular the
calculus of min-entropies, to show a pretty strong converse for the private
capacity of degraded classical-quantum-quantum (cqq-)wiretap channels, which
generalize Wyner's model of the degraded classical wiretap channel.
While the result is not completely tight, leaving some gap between the region
of error and privacy parameters for which the converse bound holds, and a
larger no-go region, it represents a further step towards an understanding of
strong converses of wiretap channels [cf. Hayashi/Tyagi/Watanabe,
arXiv:1410.0443 for the classical case].Comment: 5 pages, 1 figure, IEEEtran.cls. V2 final (conference) version,
accepted for ISIT 2016 (Barcelona, 10-15 July 2016
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