Privacy Preserving Data Mining is a method which ensures privacy of individual information during mining. Most important task involves retrieving information from multiple data bases which is distributed. The data once in the data warehouse can be used by mining algorithms to retrieve confidential information. The proposed framework has two major tasks, secure transmission and privacy of confid...

At present, data mining algorithms are largely the domain of governments, large organisations and academia where they provide useful insight into the data. However, without the ability to assure privacy protection, the availability of datasets for research purposes may be impaired. Moreover, privacypreservation is essential if data mining is to be permitted widespread use in government and comm...

Privacy Preserving Data Mining (PPDM) algorithms attempt to reduce the injuries to privacy caused by malicious parties during the rule mining process. Usually, these algorithms are designed for the semi-honest model, where participants do not deviate from the protocol. However, in the real-world, malicious parties may attempt to obtain the secret values of other parties by probing attacks or co...

The major challenge of data perturbation is to achieve the desired balance between the level of privacy guarantee and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy-preserving data mining systems and applications. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired l...

Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand su...

An interesting new direction for data mining research is the development of techniques that incorporate privacy concerns. Being an emerging field, major concentration so far has been on defining the metrics of privacy and establishing the technical feasibility of development of accurate models about aggregated data while meeting the goals of privacy. Thus the goal of the research in privacy pre...

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. The data owners or publishers may not be willing to exactly reveal the true values of their data due to various reasons, most notably privacy consider...

Resource Constrained Devices (RCD) in general construct the pervasive computing environment which are equipped with too limited resources to deploy privacy preserving data mining applications. This paper proposes a communication efficient and perturbation based privacy preserving association rule mining (ARM) algorithm for this ubiquitous computing environment. Existing cryptography based priva...

Data mining has evolved from a need to make sense of the enormous amounts of data generated by organizations. But data mining comes with its own cost, including possible threats to the confidentiality and privacy of individuals. This chapter presents a background on privacy-preserving data mining (PPDM) and the related field of statistical disclosure limitation (SDL). We then focus on privacy-p...

This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions...