CFP: Intelligent Methods for Protecting Privacy and Confidentiality in Data

CFP: Intelligent Methods for Protecting Privacy and Confidentiality in Data

Post by Marina Sok » Sat, 23 Jan 2010 03:01:02

ntelligent Methods for Protecting Privacy and Confidentiality in Data

May 30th, 2010, Ottawa, Canada,

With the increasing adoption of electronic medical/health records and
the rising use of electroinc data capture tools in clinical research,
large electronic repositories of personal health information (PHI) are
being built up. At the same time, large medical data breaches are
becoming common. Data breaches may be caused by errors committed by
insiders at the data custodian sites, or by malicious insiders. Data
breaches can also be caused by outsiders breaking into the data
repositories. These data breaches represent legal and financial
liabilities for the data custodians, and erode public trust in the
ability of data custodians to manage their PHI.

An area that has grown in importance to manage the risks from breaches
is data leak prevention (DLP). DLP technologies monitor communications
or networks to detect PHI leaks. When a leak is detected the affected
individual or organization is notified, at which point they can take
remedial action. DLP can prevent a PHI leak or detect it after it
happens. For example, if DLP is deployed to monitor email then a PHI
alert can be generated before the email is sent. If DLP is used to
monitor PHI leaks on the Internet (e.g., on peer-to-peer file sharing
networks or on web sites), then the alerts pertain to leaks that have
already occured, at which point the affected individual or data
custodian can attempt to contain the damage and stop further leaks.

Computational AI is a key enabling technology for next-generation DLP
technologies. This workshop aims to bring together researchers working
on computational tools for DLP.

Topics of interest include, but are not limited to:

+ reviews: reviews of DLP systems and methods; and reviews of PHI
leaks that are occuring.
+ methods: detection of personally identifying information in text;
detection of health information in different types of text (e.g.,
professionally written vs. lay person generated); and re-
identification risk assessment;
+ applications: monitoring the web and peer-to-peer file sharing
networks for PHI leaks; detection of PHI in email or other
communications; and tools for dealing with PHI leaks in an automated
way (e.g., de-identification).
+ evaluation: empirical evaluation of deployed systems; theoretical
methods of risk assessment; and
new methods for evaluating such systems.

Workshop Format

The workshop invites position papers describing original work in
theory and applications of intelligent methods to the problem of DLP.
Position papers will be reviewed by the Program Committee members
according to their originality, technical merit and clarity of
presentation. Each accepted paper will be allocated a maximum of 5
pages in the workshop proceedings. At least one author for each
accepted paper is expected to attend the workshop.

The workshop is planned to be interactive with discussions on the
current state and future developments in the area of DLP for PHI. All
of the workshop attendees will co-author a final report on DLP for PHI
after the workshop and submit that to a journal.


The workshop is being held in conjunction with the Canadian AI 2010
conference. Location and registration information is available at:

For more details: http://www.ehealthin