$k$-anonymity and $l$-diversity, against identity and attribute disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the $k$ -anonymity or $l$ -diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art." /> $k$ -anonymity; " /> $k$-anonymity and $l$-diversity, against identity and attribute disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the $k$ -anonymity or $l$ -diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art." /> $k$ -anonymity; " /> $k$-anonymity and $l$-diversity, against identity and attribute disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the $k$ -anonymity or $l$ -diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art." /> Accuracy-Constrained Privacy-Preserving Access Control Mechanismfor Relational Data
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Issue No.04 - April (2014 vol.26)
pp: 795-807
Zahid Pervaiz , Sch. of Electr. & Comput. Eng., Center for Educ. & Res. in Inf. Assurance & Security (CERIAS), Purdue Univ., West Lafayette, IN, USA
Walid G. Aref , Dept. of Comput. Sci., Purdue's Center for Educ. & Res. in Inf. Assurance & Security (CERIAS), West Lafayette, IN, USA
Arif Ghafoor , Sch. of Electr. & Comput. Eng., Center for Educ. & Res. in Inf. Assurance & Security (CERIAS), Purdue Univ., West Lafayette, IN, USA
Nagabhushana Prabhu , Sch. of Ind. Eng., Purdue Univ., West Lafayette, IN, USA
ABSTRACT
Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized user can still compromise the privacy of a person leading to identity disclosure. A PPM can use suppression and generalization of relational data to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity, against identity and attribute disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the k-anonymity or l-diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art.
INDEX TERMS
Access control, Privacy, Semantics, Data privacy, Query processing, Partitioning algorithms,query evaluation, Access control, privacy, $k$-anonymity
CITATION
Zahid Pervaiz, Walid G. Aref, Arif Ghafoor, Nagabhushana Prabhu, "Accuracy-Constrained Privacy-Preserving Access Control Mechanism for Relational Data", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 4, pp. 795-807, April 2014, doi:10.1109/TKDE.2013.71