Data privacy preservation is one of the most dispersed concerns on the modern business. Data sequestration concern urgency to be consigned crucially before data sets are communal on a cloud. Data anonymization refers to as concealing complicated data for heirs of data records. In this paper interrogate the complications of big data anonymization for privacy conservation from the context of scal...

Anonymization plays a key role in enabling the public release of network datasets, and yet there are few, if any, techniques for evaluating the efficacy of network data anonymization techniques with respect to the privacy they afford. In fact, recent work suggests that many state-of-the-art anonymization techniques may leak more information than first thought. In this paper, we propose techniqu...

This survey became possible due to coming request of one of Siemens Business Units to look for data anonymization solutions being presented in the market today. The customer plans to implement and deploy it within software development projects to provide offshore team with a fully functional environment without any critical data in it. Critical data are, for instance, Personal Identifiable Info...

The anonymization of sensitive microdata (e.g. medical health records) is a widely-studied topic in the research community. A still unsolved problem is the limited informative value of anonymized microdata that often rules out further processing (e.g. statistical analysis). Thus, a tradeoff between anonymity and data precision has to be made, resulting in the release of partially anonymized mic...

We present a novel de-anonymization attack on mobility trace data and social data. First, we design an Unified Similarity (US) measurement, based on which we present a US based De-Anonymization (DA) framework which iteratively de-anonymizes data with an accuracy guarantee. Then, to de-anonymize data without the knowledge of the overlap size between the anonymized data and the auxiliary data, we...

Adam Meyerson 1 Ryan Williams2 Mar h 2003 CMU-CS-03-113 S hool of Computer S ien e Carnegie Mellon University Pittsburgh, PA 15213 Abstra t The strategy of k-anonymization, proposed by Sweeney [7℄ is a te hnique for prote ting the priva y of individuals while allowing large-s ale data mining. An optimum anonymization is one whi h obtains the required priva y prote tion (insuring that every indi...

Multiclass classification problem is often solved by combing binary classifiers into ensembles. While this is required for inherently binary classifiers, such as SVM, it also provides performance advantages for other classifiers. In this paper, we address the problem of combining binary classifiers into ensembles in the differentially private data publishing framework, where the data privacy is...

Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as contains a wealth personal data. Speaker anonymization aims remove speaker information from utterance while leaving its linguistic prosodic attributes intact. State-of-the-art techniques operate by disentangling (represented via embedding) these re-synthes...

The Bitcoin system is an anonymous, decentralized crypto-currency. There are some deanonymizating techniques to cluster Bitcoin addresses and to map them to users’ identifications in the two research directions of Analysis of Transaction Chain (ATC) and Analysis of Bitcoin Protocol and Network (ABPN). Nowadays, there are also some anonymization methods such as coin-mixing and transaction remote...