Title
Precision-bounded Access Control using Sliding-Window Query Views for Privacy-preserving Data Streams
Abstract
Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for a data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. Sharing the sensitive stream data without PPM can compromise the privacy. The PPM meets privacy requirements, e.g., k-anonymity or l-diversity by generalization of stream data. Imprecision introduced by generalization can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to achieve better accuracy can lead to false-negatives if the tuples are held by PPM while the query predicate is evaluated. Administrator of an access control policy defines the imprecision bound for each query. The challenge for PPM is to optimize the delay in publishing of stream data so that the imprecision bound for the maximum number of queries is satisfied. We formulate the precision-bounded access control for privacy-preserving data streams problem, present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm. Experiments demonstrate that the proposed heuristic provides better precision for a given data stream access control policy as compared to the minimum or maximum delay heuristics proposed in existing literature.
Year
DOI
Venue
2015
10.1109/TKDE.2015.2391098
Knowledge and Data Engineering, IEEE Transactions  
Keywords
Field
DocType
access control,data stream,privacy,k-anonymity,data models,data privacy,data security,ppm,authorisation
Data mining,Data modeling,Data stream mining,Sliding window protocol,Computer science,Tuple,Data stream,k-anonymity,Access control,Information privacy
Journal
Volume
Issue
ISSN
PP
99
1041-4347
Citations 
PageRank 
References 
3
0.38
17
Authors
3
Name
Order
Citations
PageRank
Zahid Pervaiz1101.55
Arif Ghafoor22367275.36
Walid G. Aref34502419.49