In this paper we illustrate a privacy framework named Indistinguishable Privacy. Indistinguishable privacy could be deemed as the formalization of the existing privacy definitions in privacy preserving data publishing as well as secure multi-party computation. We introduce three representative privacy notions in the literature, Bayes-optimal privacy for privacy preserving data publishing, diffe...

In the recent time, privacy preserving has been studied extensively, because of the extensive explosion of sensitive information. Privacy preserving is one of the important areas that aim to provide security for secret information from unsolicited or unsanctioned disclosure. This has triggered the development of much privacy preserving technique using encryption algorithm. This work will presen...

Smart grid concept is introduced to modify the power grid by utilizing new information and communication technology. Smart grid needs live power consumption monitoring to provide required services and for this issue, bi-directional communication is essential. Security and privacy are the most important requirements that should be provided in the communication. Because of the complex design of s...

The motivation of Privacy Preserving Data Mining (PPDM) is to obtain valid data mining results without access to the original sensitive information. The different privacy preserving technique on Perturbation based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. This proposed work is based on perturbation based privacy preserving d...

The emerging public awareness and government regulations of data privacy motivate new paradigms collecting analyzing that are transparent acceptable to owners. We present a concept corresponding formats, mechanisms, theories for privatizing during collection. privacy, named Interval Privacy, enforces the raw conditional distribution on privatized be same as its unconditional over nontrivial sup...

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition du...

The freedom and transparency of information flow on the Internet has heightened concerns of privacy. Given a set of data items, clustering algorithms group similar items together. Clustering has many applications, such as customerbehavior analysis, targeted marketing, forensics, and bioinformatics. In this paper, we present the design and analysis of a privacy-preserving k-means clustering algo...

How can government agencies acquire actionable, useful information about legitimate targets, while preserving the privacy of innocent parties and holding government agencies accountable? Towards understanding this crucial issue, we present the first privacy-preserving protocol for contact chaining, an operation that law-enforcement and intelligence agencies have used effectively. Our experiment...

Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algorithms assume data is complete. In order to maintain privacy in the data mining process while cleaning data, privacy-preserving methods of data cleaning are required. In this paper, we address the problem of privacy-preserving data imput...

The recent investigation of privacy-preserving data mining has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. In an effort towards practical algorithms for privacy-preserving data mining solutions, we analyze and implement solutions to an important primitive: the privacy-preserving scalar product of two ve...