Privacy Preservation in Social Networks Sequential Publishing

Abstract

The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contain much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support, etc). Most of the anonymization works in social networks focus on publishing one instance while not considering the need for anonymizing sequential releases. However, many cases show that sequential releases may infer private information even though individual instances are anonymized. This paper studies the privacy issues of sequential releases and proposes a privacy preserving solution for this case. The proposed solution ensures three privacy requirements (users' privacy, groups' privacy and edges' privacy), and it considers the case where many users and groups may share the same profiles. Some experiments over some complex queries show that the utility of the released data is better preserved than other solutions, with regard to the privacy of users, groups and edges.

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

“…The system has been shown to have a low energy need, but it also has a significant delay and a subpar throughput due to the presence of several dummies. A model that uses a combination of safe partitioning and random (dummy) data insertion that is somewhat comparable to this one is shown in [20]. The recommended paradigm is used in order to protect temporal privacy.…”

“…Consensus based [41] Computational Complexity for General Purpose Wireless Networks EPPA [2] Crowd-sourced differential… Boneh-Goh-Nissim [7] Personal RR [9] Conventional RR [9] MTL [13] Pseudo-anonymous auth [18] Voronoi with dummies [19] Safe partitioning [20] Bilateral privacy……”