Work

Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining

Year: 2008

Type: conference-paper

Abstract: . Sequential pattern mining is a major research field in knowledge discovery and data mining. Thanks to the increasing availability of transaction data, it is now possible to provide new and improved services based on users ’ and customers ’ behavior. However, this puts the citizen’s privacy at risk. Thus, it is important to develop new privacy-preserving data mining techniques that do not alter the analysis results significantly. In this paper we propose a new approach for anonymizing sequential data by hiding infrequent, and thus potentially sensible, subsequences. Our approach guarantees that the disclosed data are k-anonymous and preserve the quality of extracted patterns. An application to a real-world moving object database is presented, which shows the effectiveness of our approach also in complex contexts. 1

Source: Institutional Research Information System University of Turin (University of Turin)

Authors: Ruggero G. Pensa, Anna Monreale, Fabio Pinelli, Dino Pedreschi

Institution: National Research Council

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, National Institute on Drug Abuse

Award: COCAINE ABUSE &EXERCISE INDUCED HEATSTROKE

OpenAlex ID: W2345771

FWCI: 3.689

Cites: 23

Cited by: 49

Related to: 10