To seek better prediction techniques, data owners of recommender systems such as Netflix sometimes make their customers’ reviews available to the public, which raises serious privacy concerns. With only a small amount of knowledge about individuals in a recommender system, an adversary may be able to re-identify users and consequently determine their item ratings. In this work, we present a rob...

While literature within the field of privacy-preserving data mining (PPDM) has been around for many years, attention has mostly been given to the perturbation and anonymization of tabular data; understanding the role of privacy over graphs and networks is still very much in its infancy. In this chapter, we survey a very recent body of research on privacy-preserving data analysis over graphs and...

Motivated by applications in privacy-preserving data publishing, we study the problem of making an undirected graph k-anonymous by adding few vertices (together with some incident edges). That is, after adding these “dummy vertices”, for every vertex degree d appearing in the resulting graph, there shall be at least k vertices with degree d. We explore three variants of vertex addition (justifi...

A wireless sensor network is a heterogeneous network consisting of a large number of tiny low-cost nodes and one or more base stations. These networks can use in various applications like military, health and commercial. However, the privacy preservation problem has drawn huge attention in the research community. This problem is exacerbated in the domain of WSNs due to the extreme resource limi...

Article history: Received 27 September 2010 Received in revised form 10 April 2011 Accepted 5 July 2011 Available online 22 July 2011 Anonymization is a practical approach to protect privacy in data. The major objective of privacy preserving data publishing is to protect private information in data whereas data is still useful for some intended applications, such as building classification mode...

To provide a high-quality mobile service in a safe way, many techniques for location anonymity have been proposed in recent years. Advanced location-based services such as mobile advertisement services may use not only users’ locations but also users’ attributes. However, the existing location anonymization methods do not consider attribute information and may result in low-quality privacy prot...

Time series anonymization is an important problem. One prominent example of time series are energy consumption records, which might reveal details of the daily routine of a household. Existing privacy approaches for time series, e.g., from the field of trajectory anonymization, assume that every single value of a time series contains sensitive information and reduce the data quality very much. ...

Data anonymization has been extensively studied and widely adopted method for privacy preserving in data publishing and sharing scenario. Data anonymization is hiding up of sensitive data for owner’s data record to avoid unidentified Risk. The privacy of an individual can be effectively preserved while some aggregate information is shared to data user for data analysis and data mining. The prop...