[TYPES/announce] Postdoc in differential privacy at the University of Pennsylvania

[Benjamin C. Pierce]

Applications are invited for a postdoc position in the theory and practice of differential privacy at the University of Pennsylvania.  An outline of the hosting project is below.

The ideal candidate will have a Ph.D. in Computer Science, a combination of strong theoretical and practical interests, and expertise in at least two of: programming languages, theoretical computer science, and systems software.  The position is for one year in the first instance, with possible renewal up to four years.  Starting date is negotiable.  Applications from women and members of other under-represented groups are particularly welcome. 

To apply, please send a CV, research statement, and the names of four people who can be asked for letters of reference to Benjamin Pierce (bcpierce at cis.upenn.edu).  Inquiries can be directed to any of the PIs: 

   Andreas Haeberlen
   Benjamin C. Pierce
   Aaron Roth   


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Putting Differential Privacy to Work 

A wealth of data about individuals is constantly accumulating in various
databases in the form of medical records, social network graphs, mobility
traces in cellular networks, search logs, and movie ratings, to name only a
few. There are many valuable uses for such datasets, but it is difficult to
realize these uses while protecting privacy. Even when data collectors try
to protect the privacy of their customers by releasing anonymized or
aggregated data, this data often reveals much more information than
intended. To reliably prevent such privacy violations, we need to replace
the current ad-hoc solutions with a principled data release mechanism that
offers strong, provable privacy guarantees. Recent research on DIFFERENTIAL
PRIVACY has brought us a big step closer to achieving this
goal. Differential privacy allows us to reason formally about what an
adversary could learn from released data, while avoiding the need for many
assumptions (e.g. about what an adversary might already know), the failure
of which have been the cause of privacy violations in the past. However,
despite its great promise, differential privacy is still rarely used in
practice. Proving that a given computation can be performed in a
differentially private way requires substantial manual effort by experts in
the field, which prevents it from scaling in practice.  

This project aims to put differential privacy to work---to build a system
that supports differentially private data analysis, can be used by the
average programmer, and is general enough to be used in a wide variety of
applications. Such a system could be used pervasively and make strong
privacy guarantees a standard feature wherever sensitive data is being
released or analyzed. Specific contributions will include ENRICHING THE
FUNDAMENTAL MODEL OF DIFFERENTIAL PRIVACY to address practical issues such
as data with inherent correlations, increased accuracy, privacy of
functions, or privacy for streaming data; DEVELOPING A DIFFERENTIALLY
PRIVATE PROGRAMMING LANGUAGE, along with a compiler that can automatically
prove programs in this language to be differentially private, and a runtime
system that is hardened against side-channel attacks; and SHOWING HOW TO
APPLY DIFFERENTIAL PRIVACY IN A DISTRIBUTED SETTING in which the private
data is spread across many databases in different administrative domains,
with possible overlaps, heterogeneous schemata, and different expectations
of privacy.  The long-term goal is to combine ideas from differential
privacy, programming languages, and distributed systems to make data
analysis techniques with strong, provable privacy guarantees practical for
general use. The themes of differential privacy are also being integrated
into Penn's new undergraduate curriculum on Market and Social Systems
Engineering.