Can Social Media Users Prevent the Use of Online Information to Characterize and Target Them?

NEW ROCHELLE, N.Y. - Nov. 20, 2017 - PRLog -- A new study examines how organizations use information people disclose on social network sites (SNS) to predict their personal characteristics and whether SNS users can successfully block certain information (and how much) to better protect their privacy. A novel analytical tool called a "cloaking device" to prevent the use of specific information and how effective it may be are discussed in an article in Big Data, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/). The article is available free on the Big Data (http://online.liebertpub.com/doi/full/10.1089/big.2017.0074) website.

The article entitled "Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals (http://online.liebertpub.com/doi/full/10.1089/big.2017.0074)" is coauthored by Daizhuo Chen, Columbia Business School(New York, NY), Samuel Fraiberger, Northeastern University (Boston, MA), and Robert Moakler and Foster Provost, Stern School of Business, New York University. They focused on the types of inferences about individuals that can be made based on their "Likes" on Facebook. They describe how organizations can be more transparent about how they use information from SNS to make personal inferences. The researchers introduce the "cloaking device" they developed and discuss how much information users need to cloak to have a significant effect on its predictive value.

"This is a landmark article," says Big Data Editor-in-Chief Vasant Dhar, Professor at the Stern School of Business and the Center for Data Science at New York University. "Given how routinely social media sites violate individual privacy for targeting, it is important for end users to get back some control over the kinds of things that are inferred about them from their surfing behavior. This paper provides a practical model for how users can cloak their identity and avoid certain types of inferences to be drawn about them."

About the Journal
Big Data (http://www.liebertpub.com/big), published quarterly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data (http://www.liebertpub.com/big) website.

About the Publisher
Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/) is a privately held, fully integrated media company known
for establishing authoritative medical and biomedical peer-reviewed journals, including OMICS: A Journal of Integrative Biology, Journal of Computational Biology, New Space, and 3D Printing and Additive Manufacturing. Its biotechnology trade magazine, GEN (Genetic Engineering & Biotechnology News), was the first in its field and is today the industry's most widely read publication worldwide. A complete list of the firm's more than 80 journals, newsmagazines, and books is available on the Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/) website.