[Elsnet-list] CFP: Intelligent Methods for Protecting Privacy and Confidentiality in Data

Nigel Collier collier at nii.ac.jp
Mon Feb 15 02:53:23 CET 2010

*** Apologies for multiple posting ***

Intelligent 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 electronic 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.ehealthinformation.ca/CAI/index.asp 

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