TY - JOUR
T1 - (τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility
AU - Khan, Razaullah
AU - Tao, Xiaofeng
AU - Anjum, Adeel
AU - Malik, Saif Ur Rehman
AU - Yu, Shui
AU - Khan, Abid
AU - Rahman, Waheed ur
AU - MALIK, HASSAN
PY - 2020/10/22
Y1 - 2020/10/22
N2 - In a real‐world scenario for privacy‐preserving data publishing, the original data are anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine, that is, correlate different releases to compromise the privacy of the individual records. Most of the literature, such as τ‐safety, τ‐safe (l, k)‐diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model (τ, m)‐slicedBucket, having a novel idea of “Cache” table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of τ‐safe (l, k)‐diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed (τ, m)‐slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.
AB - In a real‐world scenario for privacy‐preserving data publishing, the original data are anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine, that is, correlate different releases to compromise the privacy of the individual records. Most of the literature, such as τ‐safety, τ‐safe (l, k)‐diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model (τ, m)‐slicedBucket, having a novel idea of “Cache” table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of τ‐safe (l, k)‐diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed (τ, m)‐slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.
KW - Big data
KW - Electronic Health Records
KW - k-anonymity
KW - Privacy Preserving Data Publishing
KW - Privacy
KW - security
UR - http://www.scopus.com/inward/record.url?scp=85092942019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092942019&partnerID=8YFLogxK
U2 - 10.1002/ett.4130
DO - 10.1002/ett.4130
M3 - Article (journal)
SN - 2161-5748
JO - Transactions on Emerging Telecommunications Technologies
JF - Transactions on Emerging Telecommunications Technologies
ER -