Titlebook: Differential Privacy and Applications; Tianqing Zhu,Gang Li,Philip S. Yu Book 2017 Springer International Publishing AG 2017 data analysis

entrance

Uncultured

Privacy Preserving for Tagging Recommender Systems,ms and utilize differential privacy to prevent the leaking of private information when releasing the dataset. A private tagging release algorithm is presented in this chapter to provide comprehensive privacy-preserving capability for individuals and maximizing the utility of the released dataset. Th

transplantation

強(qiáng)制令

Differentially Private Spatial Crowdsourcing,e privacy breaches. In recent years there have been a number of proposals to provide the privacy preserving capability for SC applications, such as allowing the release of spatial datasets while preserving privacy. This chapter first surveys the current attempts to solve the location privacy problem

幸福愉悅感

Preliminary of Differential Privacy,, local sensitivity, and principle mechanisms that can preserve differential privacy. To make the theory accessible, an example is proposed to illustrate these concepts. In addition, utility measurements are discussed in this chapter.

珠寶

天真

Re-thinking Religious Pluralism, local sensitivity, and principle mechanisms that can preserve differential privacy. To make the theory accessible, an example is proposed to illustrate these concepts. In addition, utility measurements are discussed in this chapter.

SENT

https://doi.org/10.1007/978-3-319-62004-6data analysis; data mining; data release; differential policy; location privacy; machine learning; privacy

冒失

978-3-319-87211-7Springer International Publishing AG 2017

性別