Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/69997
Title: Competitive privacy : secure analysis on integrated sequence data
Authors: Wong, RCW
Lo, E 
Issue Date: 2010
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2010, v. 5982, p. 168-175 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Sequence data analysis has been extensively studied in the literature. However, most previous work focuses on analyzing sequence data from a single source or party. In many applications such as logistics and network traffic analysis, sequence data comes from more than one source or party. When multiple autonomous organizations collaborate and integrate their sequence data to perform analysis, sensitive business information of individual parties can be easily leaked to the other parties. In this paper, we propose the notion of competitive privacy to model the privacy that should be protected when carrying out data analysis on integrated sequence data. We propose a query restriction algorithm that can reject malicious queries with low auditing overhead. Experimental results show that our proposed method guarantees the protection of competitive privacy with only a significantly small portion of queries being restricted.
Description: 15th International Conference on Database Systems for Advanced Applications, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010
URI: http://hdl.handle.net/10397/69997
ISBN: 978-3-642-12097-8
978-3-642-12098-5
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-12098-5_13
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
Citations as of Feb 13, 2018

Page view(s)

3
Citations as of Feb 18, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.