ACM SIGCAS Computers and Society

Halavais, Alexander

doi: 10.1145/1517455.1517458pmid: N/A

The term "social informatics," at least among those who work in one or more of the various fields it helps to unite, unavoidably elicits its originator and greatest advocate, Rob Kling. Kling coined the term in order to help describe a perspective in which information technologies were studied within their social contexts. It was also used to describe a new interdiscipline, one that drew computer science and the social sciences closer together, and that recognized a literature at this nexus as an emergent, cohesive whole. Kling answered the question "What is social informatics?" explicitly not only in widely read articles on the topic, but in public talks and private conversations. As the idea of social informatics has become more widely known, another question follows it: where is social informatics? Has it made an imprint on scholarly institutions?

Sweeney, Latanya

doi: 10.1145/1517455.1517457pmid: N/A

Among the vast amounts of personal information published on the World Wide Web ("Web") and indexed by search engines are lists of names of people. Examples include employees at companies, students enrolled in universities, officers in the military, law enforcement personnel, members of social organizations, and lists of acquaintances. Knowing who works where, attends what, or affiliates with whom provides strategic knowledge to competitors, marketers, and government surveillance efforts. However, finding online rosters of people does not lend itself to keyword lookup on search engines because the keywords tend to be common expressions such as "employees" or "students." A typical search often retrieves hundreds of Web pages requiring many hours of human inspection to locate a page containing a list of names. As a result, people may falsely believe online rosters provide more privacy than they do. This paper presents RosterFinder, a simple algorithm for locating Web pages that consist predominately of a list of names. The specific names are not known beforehand. RosterFinder works by identifying rosters from candidate Web pages based on the ratio of distinct known names to distinct words appearing in the page. Accurate classification by RosterFinder depends on the set of names used. Results are reported on real Web pages using: (1) dictionary lookup employing a limited set of known names; and, (2) dictionary lookup on utilizing an extensive set of known names. Privacy implications are discussed using the example of FERPA and online student rosters.

Sweeney, Latanya

doi: 10.1145/1517455.1517456pmid: N/A

Computer Science research and practice are raising growing privacy concerns among the public and government. Computer technology's increasing ability to capture, organize, interpret and share data about individuals raises questions about what privacy practices computer science researchers should adopt, if any. These issues are already very real in ongoing research projects in the School of Computer Science (SCS) at Carnegie Mellon University, from mining databases of individual transactions, to studying how people use the web, to mounting cameras in lounges, to building hallway robots that capture data about passers by, to building intelligent workstation assistants that learn user habits. This article introduces the nature of privacy concerns related to computer science research and explains potential benefits and risks (especially of abuse and misuse). Traditional methods for providing privacy assurances in research, such as Institutional Review Boards (IRBs), are examined, and innovative new approaches, such as privacy technology, are introduced.