Today’s world of universal data exchange, there is a need to manage the risk of unintended information disclosure. Publishing the data about the individuals, without revealing sensitive information about them is an important problem. K-anonymization is the popular approach used for data publishing. The limitations of Kanonymity were overcome by methods like L-diversity, T-closeness, (alpha, K) ...

The main goal of this research is to develop and implement data privacy appropriate technique that fit with E-health system. Privacy preserving data is to develop methods without increasing the risk of misuse of the data used to generate those methods. A number of effective methods for privacy preserving data mining have been proposed. Privacy preservation of sensitive information is a key fact...

In this paper we use information retrieval metrics to evaluate the effect of a document sanitization process, measuring information loss and risk of disclosure. In order to sanitize the documents we have developed a semiautomatic anonymization process following the guidelines of Executive Order 13526 (2009) of the US Administration. It embodies two main steps: (i) identifying and anonymizing sp...

Being able to quickly create realistic honeypots is very useful for obtaining accurate information about attacker behavior. However, creating realistic honeypots requires sanitization of the original system from which the honeypot is derived. To achieve this the use of the Jeeves, a language based on faceted values, is extended to rapidly replace secret values with believable and non-interferin...

Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, it is possible to predict sensitive information carried in released data latently by utilizing data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiti...

Over the last several years, there has been an emerging interest in the development of wide-area data collection and analysis centers to help identify, track, and formulate responses to the ever-growing number of coordinated attacks and malware infections that plague computer networks worldwide. As large-scale network threats continue to evolve in sophistication and extend to widely deployed ap...