Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions

Citation

Note, M. (2009), "Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions", Online Information Review, Vol. 33 No. 4, pp. 843-844. https://doi.org/10.1108/14684520910985800

In Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions, Editor and Virginia State University Engineering Professor Ephrem Eyob selected 14 peer‐reviewed papers on current research in data mining, the discovery of actionable information patterns using statistical and artificial intelligence tools. The volume assists researchers, teachers, students and practitioners to understand data mining's “competing goals” of collecting data and preserving privacy. Topics include team building for business, agriculture production, location‐based services, national security, and social networking in urban neighborhoods.

Philip Brey, University of Twente, explores principles of information ethics that are universally valid in “Is information ethics culturally relative?” The concept of privacy has broad historical roots in sociological and anthropological discussions about its attributes and preservation in various cultures. Brey argues that the values of privacy are distinctly Western and culturally relative. Globalisation and the emergence of the internet have created a worldwide community, which requires a moral system that has yet to be developed.

In “Legal frameworks for data mining and privacy” Robert Sprague, University of Wyoming College of Business, notes the lack of legal frameworks to restrict data mining, transmission and warehousing. As technology becomes enmeshed in the daily lives of individuals, information on their activities is being stored, accessed and used. Society is developing new definitions of privacy in this information environment, but norms have changed enough that data collection has been accepted without much opposition or change to applicable laws governing such issues.

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of the data being mined. “Business collaboration by privacy‐preserving clustering” discusses a clustering method to protect the underlying attribute values of datasets with high accuracy and low cost. Authors Stanley R. M. Oliveira, Embrapa Informática Agropecuária, Brazil, and Osmar R. Zaïane, University of Alberta, note that privacy‐preserving data mining achieves the paradox of enabling data mining algorithms to use data without accessing it.

Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions provides an interdisciplinary discussion of contemporary data mining, recommendations and future trends. As the field matures, individuals, governments and corporations will continue to find common ground, balancing the individual's right to privacy and government's and industry's need to disseminate information necessary to best serve public interests. Data mining techniques of the future should be effective without dismissing the need to preserve privacy, a fundamental element of free societies.