CSE 600 & Faculty Candidate Holistic Privacy and Security...

Event Type: 
Faculty Candidate
Monday, May 1, 2017 - 14:30 to 15:30
New CS Room 120

Holistic Privacy and Security in the Age of Big Data: From Social Networks to Digital Medicine  (Room NCS 120 at 2:30p)

In the first part of this talk, I will discuss our research on de-anonymization and inference attacks and privacy
risk quantification. First, I will present a novel profile matching scheme that quantifies and shows the risk of the
profile matching attack in unstructured social networks (in which similarity in graphical structure cannot be
used for profile matching). I will show how much different attributes (both obvious identifiers such as the user
name and non-obvious identifiers such as interest similarity or sentiment variation between different posts of
users) threaten the online privacy of the users.

Once anonymized data (or profile) of an individual is re-identified, the privacy risk is not typically limited to
the target person; the risk also extends to the dependents (e.g., friends or family) of the individual. To illustrate
this, next, I will present a novel inference attack that aims a unique type of sensitive data: DNA. In particular, I
will focus on inference attacks and quantification of kin genomic privacy, using information theoretical tools. I
will show how vulnerable the genomic privacy of individuals is due to genomic data shared by their relatives,
and data available on online social networks. For this, we propose an algorithm to model such an attack using
(i) available genomic data of a subset of family members, (ii) high order correlations between the nucleotides on
the DNA, (iii) phenotype information, and (iv) publicly known genomic background. For the efficiency of such
an algorithm, we represent this attack as an inference problem and develop a novel graph-based algorithm.

In the remaining of the talk, I will introduce a new protection mechanism, GenoGuard, based on a newly
proposed cryptographic primitive called honey-encryption. Considering the high sensitivity and longevity of
health-related data, GenoGuard is able to provide security against brute-force attacks (by attackers with
unlimited computational power). I will also discuss about our ongoing research about its extensions such as
privacy-preserving database update and synthetic data generation.
Erman Ayday is an assistant professor of computer science at Bilkent University, Ankara, Turkey. Before that
he was a post-doctoral Researcher at EPFL, Switzerland, working with Prof. Jean-Pierre Hubaux. He received
his M.S. and Ph.D. degrees from School of Electrical and Computer Engineering (ECE), Georgia Institute of 
Technology, Atlanta, GA, in 2007 and 2011, respectively under the supervision of Dr. Faramarz Fekri. Erman’s
research interests include privacy-enhancing technologies (including big data and genomic privacy), applied
cryptography and data security, trust and reputation management, and inference from big data. Erman is the
recipient of Distinguished Student Paper Award at IEEE S&P 2015, 2010 Outstanding Research Award from
CSIP at Georgia Tech, and 2011 ECE Graduate Research Assistant Excellence Award from Georgia Tech.
Erman has published more than 50 peer-reviewed papers in prestigious venues including ACM CCS, IEEE
S&P, and IEEE TDSC. He has been also serving in the program committee of many conferences including
ACM CCS, NDSS, and AsiaCCS. Other various accomplishments of Erman include several patents, research
grants, and H2020 Marie Curie individual fellowship. 


Hosted By: 
Nick Nikiforakis
Computed Event Type: 
Event Title: 
CSE 600 & Faculty Candidate Holistic Privacy and Security...