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 set of simple algorithms 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.

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*Institute for Software Research International, School of Computer Science, Carnegie Mellon University

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