[Busec] The Charles River Privacy Day @BU, Friday November 15

Ran Canetti canetti at bu.edu
Sun Nov 10 23:42:24 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 


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.

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