The COVID-19 epidemic has made online video conferencing extremely popular throughout the world, with many schools, companies and government sectors using applications (e.g., Zoom, Google Meet) in a daily basis. These also provide local or cloud recording services, which allow replay sharing of conference recordings (VCRs) later time. Such convenience, however, can easily cause infringement pri...

We are experiencing the expanding use of location-based services such as AT&T TeleNav GPS Navigator and Intel’s Thing Finder. Existing locationbased services have collected a large amount of location data, which have great potential for statistical usage in applications like traffic flow analysis, infrastructure planning and advertisement dissemination. The key challenge is how to wisely use th...

Social networks have received dramatic interest in research and development. In this chapter, we survey the very recent research development on privacy-preserving publishing of graphs and social network data. We categorize the state-of-the-art anonymization methods on simple graphs in three main categories: K-anonymity based privacy preservation via edge modification, probabilistic privacy pres...

The issue of data privacy is considered a significant hindrance to the development and industrial applications of database publishing and data mining techniques. Among many privacy-preserving methodologies, data perturbation is a popular technique for achieving a balance between data utility and information privacy. It is known that the attacker’s background information about the original data ...

Camouflaging data by generating fake information is a wellknown obfuscation technique for protecting data privacy. The effectiveness of this technique in protecting users’ privacy highly depends on the resemblance of fake information to reality, such that an adversary cannot easily filter such fake information out. In this paper, we focus on a very sensitive and increasingly exposed type of dat...

Privacy-preserving data mining (PPDM) is an important problem and is currently studied in three approaches: the cryptographic approach, the data publishing, and the model publishing. However, each of these approaches has some problems. The cryptographic approach does not protect privacy of learned knowledge models and may have performance and scalability issues. The data publishing, although is...

The large amount of data is used in corporate and government institution. The sequence of releases is more identify by adversary. So, the user’s privacy is violated. To avoid this to use privacy protection techniques are applied here. Privacy preserving serial data publishing on dynamic databases has relied on unrealistic assumptions of the nature of dynamic databases. In many applications, som...

As a serious concern in data publishing and analysis, privacy preserving data processing has received a lot of attention. Privacy preservation often leads to information loss. Consequently, we want to minimize utility loss as long as the privacy is preserved. In this chapter, we survey the utility-based privacy preservation methods systematically. We first briefly discuss the privacy models and...