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Context-Based Privacy Model


University of Arkansas at Little Rock researchers from the College of Engineering and Information Technology have developed a new model to manage the “vast ocean” of user-generated content being generated by the ever-growing social networking sites including Facebook and Twitter.

Nitin Agarwal, assistant professor in EIT’s Department of Information Science, and his doctoral student, M. Venkata Swamy, worked with Srini Ramaswamy, former chair of the UALR Computer Science Department and now director of industrial software systems ABB India, to develop a Context-Based Privacy Model. The model leverages intelligent, scalable, adaptive, and robust pattern-matching algorithms to allow Internet sites to automatically adjust privacy needs of consumers or organization to the context in which the data is accessed.

Their paper on the project was awarded “Best Paper” and was presented at the Second International Symposium on Privacy and Security Applications held in conjunction with the IEEE International Conference on Privacy, Security, Risk, and Trust this week in Minneapolis, Minnesota.

.“With the advent of social media websites such as Facebook, MySpace, and Twitter, and social health websites such as PatientsLikeMe that help people with health conditions connect with people with like conditions, a vast ocean of user-generated content has been created — including non-sensitive information as well as sensitive demographic, financial, or health-related data,” Agarwal said. “As a result, users may be unknowingly granting access to their data, leading to grave privacy concerns.”

The existing research on developing privacy models, although seemly persuasive, are essentially based on user, role, or service identification. Such models are incapable of automatically adjusting privacy needs of consumers or organizations to the context in which the data is accessed.

“In this work, we propose a Context Based Privacy Model (CBPM), which leverages the automatic context identification of the information consumer borrowing concepts from Object Oriented methodology,” the researchers said. A context could be defined as a secure or non-secure location, family members, or group of friends, etc.

“Considering numerous pieces of information such as name, telephone number, e-mail address, age, gender, items purchased online, social interactions each individual generates; and the number of contexts created, the CBPM matrix could quickly become huge and unmanageable.”

The UALR team addresses that problem by leveraging intelligent, scalable, adaptive, and robust pattern-matching algorithms to compress the matrix, making it more manageable.

“Our work has shown the necessity of avant-garde privacy models dealing with the challenges of new types of information sources, creating a vast ocean of data with intricate access requirements and constraints, forcing us to think beyond the existing user, role, or service-based privacy models,” Agarwal said. “The proposed work is unique, one of its kind emphasizing on the context more importantly than the content, with far-reaching implications in the privacy as well as the information security area.”

The research was supported in part by grants from the Office of Naval Research and the National Science Foundation.


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