Results 161 to 170 of about 7,115,390 (197)

Approximate algorithms for K-anonymity

Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007
When a table containing individual data is published, disclosure of sensitive information should be prohibitive. A naive approach for the problem is to remove identifiers such as name and social security number. However, linking attacks which joins the published table with other tables on some attributes, called quasi-identifier, may reveal the ...
Hyoungmin Park, Kyuseok Shim
openaire   +1 more source

The Classification of k-anonymity Data

2011 Seventh International Conference on Computational Intelligence and Security, 2011
In recent years, anonymization methods have emerged as an important tool to preserver individual privacy when relasing privacy sensitive data. All of these methods are under different privacy and utility assumption. But there has been little research addressing how to effectively use the anonymized data for data mining.
Bingchun Lin, Guohua Liu
openaire   +1 more source

k-anonymous message transmission

Proceedings of the 10th ACM conference on Computer and communications security, 2003
Informally, a communication protocol is sender k - anonymous if it can guarantee that an adversary, trying to determine the sender of a particular message, can only narrow down its search to a set of k suspects. Receiver k-anonymity places a similar guarantee on the receiver: an adversary, at best, can only narrow down the possible receivers to a set ...
Luis von Ahn, Andrew Bortz, Nicholas J. Hopper   +2 more
openaire   +1 more source

Towards optimal k-anonymization

Data & Knowledge Engineering, 2008
When releasing microdata for research purposes, one needs to preserve the privacy of respondents while maximizing data utility. An approach that has been studied extensively in recent years is to use anonymization techniques such as generalization and suppression to ensure that the released data table satisfies the k-anonymity property.
Tiancheng Li, Ninghui Li 0001
openaire   +1 more source

Clustering-Based k-Anonymity

2012
Privacy is one of major concerns when data containing sensitive information needs to be released for ad hoc analysis, which has attracted wide research interest on privacy-preserving data publishing in the past few years. One approach of strategy to anonymize data is generalization.
Xianmang He, Huahui Chen 0001, Yefang Chen, Yihong Dong, Peng Wang 0027, Zhenhua Huang 0005   +5 more
openaire   +1 more source

k-Anonymization with Minimal Loss of Information

IEEE Transactions on Knowledge and Data Engineering, 2007
The technique of k-anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose two information-theoretic measures for capturing the amount of ...
Aristides Gionis, Tamir Tassa
openaire   +1 more source

k-Anonymization in the Presence of Publisher Preferences

IEEE Transactions on Knowledge and Data Engineering, 2011
Privacy constraints are typically enforced on shared data that contain sensitive personal attributes. However, owing to its adverse effect on the utility of the data, information loss must be minimized while sanitizing the data. Existing methods for this purpose modify the data only to the extent necessary to satisfy the privacy constraints, thereby ...
Rinku Dewri, Indrajit Ray, Indrakshi Ray, L. Darrell Whitley   +3 more
openaire   +1 more source

K-anonymity on sensitive transaction items

2011 IEEE International Conference on Granular Computing, 2011
K-anonymity-based techniques [9], [11], [15]–[17] have been the main anonymization techniques on relational data ad transactional data to protect privacy against re-identification attacks. Assuming the existence of both sensitive attributes and quasi-identifier (QI) attributes, a relational dataset D is k-anonymous if every record in D has at least k-1
Shyue-Liang Wang, Yu-Chuan Tsai, Hung-Yu Kao, Tzung-Pei Hong   +3 more
openaire   +1 more source

k-Anonymity

2021
Sabrina De Capitani di Vimercati, Pierangela Samarati   +1 more
openaire   +1 more source

From K-anonymity to Differential Privacy back to K -anonymity!

2013
The research community has left no stone unturned in devising strategies for both syntactic and semantic privacy definitions. The literature on privacy protection reveals that no privacy model is capable of incorporating growing demands of data publication (e.g., the adversarial background, needs of data publisher, constraints on underlying dataset etc.
Anjum, Adeel, Raschia, Guillaume, Gelgon, Marc   +2 more
openaire   +1 more source