[Theory-Talk] 20160510 -- Robust Traceability from Trace Amounts

[Yu-Chi Chen]

Topic: *Robust Traceability from Trace Amounts*

Speaker: *Salil P. Vadhan*

Date: *2016/05/10 10:30*

Location: *2F at **Institute of Statistical Science, Academia Sinica*

*(Coffee break at 10:10am)*


Abstract:

The privacy risks inherent in the release of a large number of summary
statistics were illustrated by Homer et al. (PLoS Genetics, 2008), who
considered the case of 1-way marginals of SNP allele frequencies obtained
in a genome-wide association study: Given a large number of minor allele
frequencies from a case group of individuals diagnosed with a particular
disease, together with the genomic data of a single target individual and
statistics from a sizable reference dataset independently drawn from the
same population, an attacker can determine with high confidence whether or
not the target is in the case group. In this work we describe and analyze a
simple attack that succeeds even if the summary statistics are
significantly distorted, whether due to measurement error or noise
intentionally introduced to protect privacy. Our attack only requires that
the vector of distorted summary statistics is close to the vector of true
marginals in L1 norm. Moreover, the reference pool required by previous
attacks can be replaced by a single sample drawn from the underlying
population. The new attack, which is not specific to genomics and which
handles Gaussian as well as Bernoulli data, significantly generalizes
recent lower bounds on the noise needed to ensure differential privacy
(Bun, Ullman, and Vadhan, STOC 2014; Steinke and Ullman, 2015), obviating
the need for the attacker to control the exact distribution of the data.

Joint work with Cynthia Dwork, Adam D. Smith, Thomas Steinke, and Jon
Ullman.
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