Workshop on Intelligent Methods for Protecting Privacy and Confidentiality in Data
http://www.ehealthinformation.ca/CAI/index.asp, May 30th, 2010, Ottawa, with Canadian AI 2010 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. For more information: http://www.ehealthinformation.ca/CAI/index.asp Regards, Marina Sokolova, Ph.D., Research Associate, Research Institute, Children's Hospital of Eastern Ontario, 401 Smyth Rd., office R306a, Ottawa, Ontario, Canada, K1H 8L1, phone: 613-737-7600, ext. 4104; fax: 613-731-1374;
_______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
