Featured Research

from universities, journals, and other organizations

New model to help organize, keep private 'vast ocean' of social network data

Date:
August 27, 2010
Source:
University of Arkansas at Little Rock
Summary:
Researchers 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.

Professors and a graduate student from the College of Engineering and Information Technology at the University of Arkansas at Little Rock (UALR) 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.

Related Articles


Dr. Nitin Agarwal, assistant professor in EIT's Department of Information Science, and his doctoral student M. Venkata Swamy worked with Dr. Srini Ramaswamy, former chair of the UALR Department of Computer Science and now director of Industrial Software Systems at 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 organizations to the context in which the data is accessed.

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

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 Institute of Electrical and Electronic Engineering (IEEE) International Conference on Privacy, Security, Risk, and Trust Aug. 20-22 in Minneapolis, Minn. Only 13 percent of papers submitted at the highly competitive conference are presented.

"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."

In recent years, companies' data information centers are facing increasing federal regulations due to these privacy concerns, forcing them to modify their privacy information-handling policies continuously. The existing research on developing privacy models, although seem 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."


Story Source:

The above story is based on materials provided by University of Arkansas at Little Rock. Note: Materials may be edited for content and length.


Cite This Page:

University of Arkansas at Little Rock. "New model to help organize, keep private 'vast ocean' of social network data." ScienceDaily. ScienceDaily, 27 August 2010. <www.sciencedaily.com/releases/2010/08/100826215935.htm>.
University of Arkansas at Little Rock. (2010, August 27). New model to help organize, keep private 'vast ocean' of social network data. ScienceDaily. Retrieved November 27, 2014 from www.sciencedaily.com/releases/2010/08/100826215935.htm
University of Arkansas at Little Rock. "New model to help organize, keep private 'vast ocean' of social network data." ScienceDaily. www.sciencedaily.com/releases/2010/08/100826215935.htm (accessed November 27, 2014).

Share This


More From ScienceDaily



More Computers & Math News

Thursday, November 27, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Predictions Of Tablets' Demise Sound Familiar

Predictions Of Tablets' Demise Sound Familiar

Newsy (Nov. 26, 2014) The tablet's days are numbered, at least according to a recent IDC report. The market-research firm paints a grim outlook for tablets. Video provided by Newsy
Powered by NewsLook.com
Today's Prostheses Are More Capable Than Ever

Today's Prostheses Are More Capable Than Ever

Newsy (Nov. 26, 2014) Advances in prosthetics are making replacement body parts stronger and more lifelike than they’ve ever been. Video provided by Newsy
Powered by NewsLook.com
FCC Forces T-Mobile To Alert Customers Of Data Throttling

FCC Forces T-Mobile To Alert Customers Of Data Throttling

Newsy (Nov. 25, 2014) T-Mobile and the FCC have reached an agreement requiring the company to alert customers when it throttles their data speeds. Video provided by Newsy
Powered by NewsLook.com
Symantec Uncovers Sophisticated Spying Malware Regin

Symantec Uncovers Sophisticated Spying Malware Regin

Newsy (Nov. 24, 2014) A Symantec white paper reveals details about Regin, a spying malware of unusual complexity which is believed to be state-sponsored. Video provided by Newsy
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:

Breaking News:

Strange & Offbeat Stories


Space & Time

Matter & Energy

Computers & Math

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile: iPhone Android Web
Follow: Facebook Twitter Google+
Subscribe: RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins