Cloud Data Protection to the Masses

Speaker:        Dr. Elaine Shi
                University of Maryland, College Park

Title:          "Cloud Data Protection to the Masses"

Date:           Thursday, 7 June 2012

Time:           4:00pm - 5:00pm

Venue:          Room 3416 (via lifts 17/18), HKUST

Abstract:

Although cloud computing promises numerous benefits, including lower
costs, rapid scaling, easier maintenance, and ubiquitous availability, a
key challenge is how to protect users' data in the cloud. Today, users
effectively lose control of their data in the cloud, and if either the
cloud infrastructure or applications are compromised, users' privacy will
be at risk. The ubiquitous concern over cloud data privacy demands a
paradigm shift, such that users can retain control of their data in the
cloud, and verify that the cloud providers have correctly enforced their
privacy policies.

In this talk, I will describe several enabling technologies towards this
vision. Specifically, I will talk about 1) how to safeguard users' data
against potentially compromised applications; 2) how to safeguard users'
data against a potentially compromised computation provider; and 3) how to
safeguard users' data against a potentially compromised storage provider.
I will also talk about our ongoing effort at integrating these
technologies to provide a cloud infrastructure which offers data
protection at the platform level. In this way, users can benefit from the
rich cloud applications without worrying about the privacy of their data;
and application developers can focus on developing functionality while
offloading the burden of providing security and privacy to the cloud
platform.


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Biography:

Elaine Shi is an Assistant Professor at the University of Maryland,
College Park. She obtained her Ph.D. and Masters degrees in Computer
Science from Carnegie Mellon University, and her B.E. from Tsinghua
University. Before joining University of Maryland, she was a Member of
Research Staff at Palo Alto Research Center (PARC), and a research
scientist at UC Berkeley.

Elaine Shi is broadly interested in the general area of security, privacy,
and applied cryptography. In her research, she takes a unique approach
where she combines theoretic innovations with practical system design and
implementation. Her research spans a wide range of topics, including
computation on encrypted data, privacy-preserving data mining, system
security, sensor network and vehicular network security, usable
authentication, secure storage systems, and so on.
She has published ~40 scholarly publications, and her work has received
more than 2000 citations. Aside from security and privacy, Elaine is also
interested in data mining. In particular, she and her team won the
IJCNN/Kaggle Social Network Challenge in 2011.