Data stream can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process. Examples of data streams include computer network traffic, phone conversations, web searches and sensor data etc. These data sets need to be analyzed for identifying trends and patterns, which help us in isolating anomalies and predicting future behavior. However,...

Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of datamining-as-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the ...

The study of privacy preserving data mining has become more important in recent years due to the increasing amount of personal data in public, the increasing sophistication of data mining algorithms to leverage this information, and the increasing concern of privacy 1 Corresponding author Hiding Collaborative Recommendation Association Rules on Horizontally Partitioned Data

With the emergence of Internet, it is now possible to connect and access sources of information and databases throughout the world. At the same time, this raises many questions regarding the privacy and the security of the data, in particular how to mine useful information while preserving the privacy of sensible and confidential data. Privacy-preserving data mining is a relatively new but rapi...

Privacy is one of the most important properties that an information system must satisfy. In these systems, there is a need to share information among different, not trusted entities, and the protection of sensible information has a relevant role. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy preserving when data mining techniques a...

In real-life data mining applications, organizations cooperate by using each other’s on the same task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving (PPDM) practices involve rules techniques that allow parties to collaborate applications while keeping their private. The objective of this paper is present a number PPDM protocols show...

With the rapid development of modern data collection and data warehouse technologies, data mining is becoming more and more a standard practice. Accompanying this trend, preserving privacy in certain data becomes a challenge to data mining applications in many fields, especially in medical, financial and homeland security fields. We present a class of novel privacy-preserving data distortion me...