Show simple item record

dc.contributor.authorXue, Mingqiang
dc.contributor.authorPapadimitriou, Panagiotis D.
dc.contributor.authorRaïssi, Chedy
dc.contributor.authorKalnis, Panos
dc.contributor.authorPung, Hungkeng
dc.date.accessioned2015-08-04T06:24:03Z
dc.date.available2015-08-04T06:24:03Z
dc.date.issued2011
dc.identifier.citationXue, M., Papadimitriou, P., Raïssi, C., Kalnis, P., & Pung, H. K. (2011). Distributed Privacy Preserving Data Collection. Lecture Notes in Computer Science, 93–107. doi:10.1007/978-3-642-20149-3_9
dc.identifier.isbn9783642201486
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-20149-3_9
dc.identifier.urihttp://hdl.handle.net/10754/564335
dc.description.abstractWe study the distributed privacy preserving data collection problem: an untrusted data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multi-attributed record which contains both non-sensitive (e.g., quasi-identifiers) and sensitive information (e.g., a particular disease), and submits it to the data collector. Assuming T is the table formed by all the respondent data records, we say that the data collection process is privacy preserving if it allows the data collector to obtain a k-anonymized or l-diversified version of T without revealing the original records to the adversary. We propose a distributed data collection protocol that outputs an anonymized table by generalization of quasi-identifier attributes. The protocol employs cryptographic techniques such as homomorphic encryption, private information retrieval and secure multiparty computation to ensure the privacy goal in the process of data collection. Meanwhile, the protocol is designed to leak limited but non-critical information to achieve practicability and efficiency. Experiments show that the utility of the anonymized table derived by our protocol is in par with the utility achieved by traditional anonymization techniques. © 2011 Springer-Verlag.
dc.publisherSpringer Nature
dc.titleDistributed privacy preserving data collection
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalDatabase Systems for Advanced Applications
dc.conference.date22 April 2011 through 25 April 2011
dc.conference.name16th International Conference on Database Systems for Advanced Applications, DASFAA 2011
dc.conference.locationHong Kong
dc.contributor.institutionComputer Science Department, National University of Singapore, Singapore
dc.contributor.institutionStanford University, United States
dc.contributor.institutionINRIA Nancy, France
kaust.personKalnis, Panos


This item appears in the following Collection(s)

Show simple item record