Achieving k-anonymity privacy protection using generalization and suppression. IJUFKS. 2002 Beth has diabetes NAME DOB SEX ZIP BETH 10/21/74 M 528705 BOB 4/5/85 M 528975 KEELE 8/7/74 F 528741 MIKE 6/6/65 M 528985 LOLA 9/6/76 F 528356 BILL 8/7/69 M 528459 DOB SEX ZIP DISEASE
Privacy Preserving in Data Mining K-anonymity is technique which gives the new and more efficient ways for anonymized data and it preserve patterns during whole anonymization. The k-anonymity model defines the whole privacy of output of process and that process is not by itself. It is simple and well understood model [10,12,16]. K-anonymity is main privacy protection model. K K-anonymity - WikiMili, The Best Wikipedia Reader k-anonymity is a property possessed by certain anonymized data.The concept of k-anonymity was first introduced by Latanya Sweeney and Pierangela Samarati in a paper published in 1998 [1] as an attempt to solve the problem: "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re Privacy Protection: p-Sensitive k-Anonymity Property describe k-anonymity privacy protection model. We show that k-anonymity protects against identity disclosure [11], but it fails to protect against attribute disclosure [11]. Second, we introduce a new privacy protection model called p-sensitive k-anonymity that extends the existing model and protects against both identity and attribute disclosure.
k-anonymity is an intuitive and widely used method for modifying data for privacy protection. k-anonymity anonymizes data by hiding the individual record in a group of similar records, thus significantly reducing the possibility that the individual can be identified.
Nov 26, 2016 k-anonymity and SQL Server – Information with Insight Jan 09, 2017 Protecting Privacy Using k-Anonymity | Journal of the
Apr 20, 2007
Achieving Anonymity via Clustering proposed the k-anonymity model for privacy where non-key attributes that leak information are suppressed or general-ized so that, for every record in the modified table, there are at least k¡1 other records having exactly the same values for quasi-identifiers. We propose a new method for anonymiz-