[Busec] Fwd: [cis-seminars] Charles River Privacy Day, Friday, Nov. 15

Sharon Goldberg goldbe at cs.bu.edu
Thu Nov 14 10:15:42 EST 2013

 *Reminder: Happening Tomorrow!*

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


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:

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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