Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are
generated by IoT devices. Those data after analytics provide significant information that …

Releasing social network data could seriously breach user privacy. User profile and
friendship relations are inherently private. Unfortunately, sensitive information may be …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

The increasing popularity of social media has attracted a huge number of people to
participate in numerous activities on a daily basis. This results in tremendous amounts of …

Nowadays, many computer and communication systems generate graph data. Graph data
span many different domains, ranging from online social network data from networks like …

Social network data is widely shared, transferred and published for research purposes and
business interests, but it has raised much concern on users' privacy. Even though users' …

In this paper, we analyze and systematize the state-ofthe-art graph data privacy and utility
techniques. Specifically, we propose and develop SecGraph (available at [1]), a uniform and …

Social network data is widely shared, transferred and published for research purposes and
business interests, but it has raised much concern on users' privacy. Even though users' …

The massive generation, extensive sharing, and deep exploitation of data in the big data era
have raised unprecedented privacy threats. To address privacy concerns, various privacy …