Distributed privacy preserving data collection

Handle URI:
http://hdl.handle.net/10754/564335
Title:
Distributed privacy preserving data collection
Authors:
Xue, Mingqiang; Papadimitriou, Panagiotis D.; Raïssi, Chedy; Kalnis, Panos ( 0000-0002-5060-1360 ) ; Pung, Hungkeng
Abstract:
We 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Springer Science + Business Media
Journal:
Database Systems for Advanced Applications
Conference/Event name:
16th International Conference on Database Systems for Advanced Applications, DASFAA 2011
Issue Date:
2011
DOI:
10.1007/978-3-642-20149-3_9
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642201486
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXue, Mingqiangen
dc.contributor.authorPapadimitriou, Panagiotis D.en
dc.contributor.authorRaïssi, Chedyen
dc.contributor.authorKalnis, Panosen
dc.contributor.authorPung, Hungkengen
dc.date.accessioned2015-08-04T06:24:03Zen
dc.date.available2015-08-04T06:24:03Zen
dc.date.issued2011en
dc.identifier.isbn9783642201486en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-20149-3_9en
dc.identifier.urihttp://hdl.handle.net/10754/564335en
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.en
dc.publisherSpringer Science + Business Mediaen
dc.titleDistributed privacy preserving data collectionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalDatabase Systems for Advanced Applicationsen
dc.conference.date22 April 2011 through 25 April 2011en
dc.conference.name16th International Conference on Database Systems for Advanced Applications, DASFAA 2011en
dc.conference.locationHong Kongen
dc.contributor.institutionComputer Science Department, National University of Singapore, Singaporeen
dc.contributor.institutionStanford University, United Statesen
dc.contributor.institutionINRIA Nancy, Franceen
kaust.authorKalnis, Panosen
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