[NRG] Fwd: The Charles River Privacy Day @BU, Friday November 15

Sharon Goldberg goldbe at cs.bu.edu
Mon Nov 11 08:24:21 EST 2013

* Coming up this friday! forward to your colleagues! *

You are cordially invited to the Charles River Privacy Day, which will take
place this on Friday November 15, in the Hariri Institute at Boston
University (111 Cummington Mall, MCS 180). There will be four talks
covering different aspects of the challenge of protecting privacy of
personal information in public databases.

Also, an introductory talk on data privacy will be given on Wednesday,
November 13th at 3pm at the same location (Hariri Institute), by Professor
Adam Smith of Penn State.


No registration is required; attendees may show up at the Hariri Institute
on the days of the events. Abstracts and schedules below.

The Charles River Privacy Day is co-organized by Ran Canetti, Sharon
Goldberg, Kobi Nissim, Sofya Rashkhodnikova, Leo Reyzin, and Adam Smith,
and is sponsored by the Center for Reliable Information Systems and Cyber
Security and by the Hariri Institute for Computing at Boston University.


9 – 9:30am Light breakfast
9:15am Welcome and Introductory Remarks
9:30am Privately Solving Allocation Problems: Aaron Roth (University of
10:45am Break
11:00am Fingerprinting Codes, Traitor-Tracing Schemes, and the Price of
Differential Privacy: Jonathan Ullman (Harvard University)
12:00pm Lunch (provided)
2:00pm Genome Hacking: Yaniv Erlich (MIT and Whitehead Institute)
3:15pm Break
3:30pm Privacy and coordination: Computing on databases with endogenous
participation: Katrina Ligett (California Institute of Technology)

The two closest hotels to the Hariri Institute are the Hotel Commonwealth,
617-933-5000, and the Hotel Buckminster, 800-727-2825. The Hotel
Commonwealth offers a BU rate; just mention that you are attending an event
at BU.



Pinning Down "Privacy" in Statistical Databases: Adam Smith, Penn State
    3:00-4:30 pm on Wednesday, November 13, 2013
    Hariri Institute, MCS180, 111 Cummington St, Boston, MA

Abstract: Consider an agency holding a large database of sensitive personal
information -- medical records, census survey answers, web search records,
or genetic data, for example. The agency would like to discover and
publicly release global characteristics of the data (say, to inform policy
and business decisions) while protecting the privacy of individuals'
records. This problem is known variously as "statistical disclosure
control", "privacy-preserving data mining" or "private data analysis". We
will begin by discussing what makes this problem difficult, and exhibit
some of the problems that plague simple attempts at anonymization.
Motivated by this, we will discuss "differential privacy", a rigorous
definition of privacy in statistical databases that has received
significant recent attention. Finally, we survey some basic techniques for
designing differentially private algorithms. This introductory talk
complements a day of talks on data privacy research to be held at BU on
Friday, November 15: http://www.bu.edu/cs/charles-river-privacy-day/

Bio: Adam Smith is an associate professor in the Department of Computer
Science and Engineering at Penn State, currently on sabbatical at Boston
University. His research interests lie in cryptography, privacy and their
connections to information theory, quantum computing and statistics. He
received his Ph.D. from MIT in 2004 and was subsequently a visiting scholar
at the Weizmann Institute of Science and UCLA. In 2009, he received a
Presidential Early Career Award for Scientists and Engineers (PECASE).


Privately Solving Allocation Problems
Aaron Roth, University of Pennsylvania

Abstract: In this talk, we’ll consider the problem of privately solving the
classical allocation problem: informally, how to allocate items so that
most people get what they want. Here, the data that we want to keep private
is the valuation function of each person, which specifies how much they
like each bundle of goods. This problem hasn’t been studied before, and for
good reason: its plainly impossible to solve under the constraint of
differential privacy. The difficulty is that publishing what each person i
receives in a high-welfare allocation might necessarily have to reveal a
lot about the preferences of person i, which is what we are trying to keep
private! What we show is that under a mild relaxation of differential
privacy (in which we require that no adversary who learns the allocation of
all people j != i — but crucially not the allocation of person i — should
be able to learn much about the valuation function of player i) the
allocation problem is solvable to high accuracy, in some generality. Our
solution makes crucial use of Walrasian equilibrium prices, which we use as
a low information way to privately coordinate a high welfare allocation.

Bio: Aaron Roth is the Raj and Neera Singh assistant professor of Computer
and Information Sciences at the University of Pennsylvania. Prior to this,
he was a postdoctoral researcher at Microsoft Research, New England, and
earned his PhD at Carnegie Mellon University. He is the recipient of a
Yahoo! Academic Career Enhancement Award, and an NSF CAREER award. His
research focuses on the algorithmic foundations of data privacy, game
theory and mechanism design, and the intersection of the two topics.


Genome Hacking
Yaniv Erlich, MIT and Whitehead Institute

Abstract: Sharing sequencing datasets without identifiers has become a
common practice in genomics. We developed a novel technique that uses
entirely free, publicly accessible Internet resources to fully identify
individuals in these studies. I will present quantitative analysis about
the probability of identifying US individuals by this technique. In
addition, I will demonstrate the power of our approach by tracing back the
identities of multiple whole genome datasets in public sequencing

Short bio: Yaniv Erlich is a Fellow at the Whitehead Institute for
Biomedical Research. Erlich received his Ph.D. from Cold Spring Harbor
Laboratory in 2010 and B.Sc. from Tel-Aviv University in 2006. Prior to
that, Erlich worked in computer security and was responsible for conducting
penetration tests on financial institutes and commercial companies. Dr.
Erlich’s research involves developing new algorithms for computational
human genetics.


Privacy and coordination: Computing on databases with endogenous
Katrina Ligett, Assistant Prof of Computer Science and Economics, Caltech

Abstract: We propose a simple model where individuals in a
privacy-sensitive population decide whether or not to participate in a
pre-announced noisy computation by an analyst, so that the database itself
is endogenously determined by individuals’ participation choices. The
privacy an agent receives depends both on the announced noise level, as
well as how many agents choose to participate in the database. Each agent
has some minimum privacy requirement, and decides whether or not to
participate based on how her privacy requirement compares against her
expectation of the privacy she will receive if she participates in the
computation. This gives rise to a game amongst the agents, where each
individual’s privacy if she participates, and therefore her participation
choice, depends on the choices of the rest of the population.

We investigate symmetric Bayes-Nash equilibria, which in this game consist
of threshold strategies, where all agents whose privacy requirements are
weaker than a certain threshold participate and the
remaining agents do not. We characterize these equilibria, which depend
both on the noise announced by the analyst and the population size; present
results on existence, uniqueness, and multiplicity; and
discuss a number of surprising properties they display.

Joint work with Arpita Ghosh

Brief bio: Katrina Ligett is an assistant professor of computer science and
economics at Caltech. Before joining Caltech in 2011, she received her PhD
from Carnegie Mellon and spent two years as a postdoc at Cornell.

Sharon Goldberg
Computer Science, Boston University
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