Anonymization Techniques in Social Networks

Yuping, Yan [Yan, Yuping, szerző] Programozási Nyelvek és Fordítóprogramok Tanszék (ELTE / IK); PhD Informatika Doktori Iskola (ELTE / IK); Peter, Ligeti [Ligeti, Péter (Matematika és szá...), szerző] Komputer Algebra Tanszék (ELTE / IK)

Angol nyelvű Absztrakt / Kivonat (Egyéb konferenciaközlemény) Tudományos
Megjelent: Zoltán Horváth. Collection of Abstracts: 13th Joint Conference on Mathematics and Informatics. (2020) p. Collection of Abstracts 13th Joint Conference on Mathematics and Informatics October 1-3, 2020 , 2 p.
    Azonosítók
    • MTMT: 32006440
    Támogatások:
    • Tématerületi Kiválósági Program 2019(ED_18-1-2019-0030) Támogató: NKFIH
    Due to the needs of social analysis, behavior prediction and personal customization, social network data is collected and released in large quantities. In this process, the abuse of private information is a widespread problem. The contradiction between the public service of social networks and the protection of individual private information has become one of the focus issues of current privacy protection. There are mainly three quizzes in cloud computing environment: large data privacy protection, data credibility and access control. K-anonymization [1.] as a main data anonymity protection technology, shows its advantages in neighbour attacks and privacy-preserving data publishing (PPDP). However, most of the researches focus on static, one-time release. Thus, these models can not prevent data analysis by collision attacks, and data anonymization in cloud computing environment. In overview, it is a survey paper, and we compare and analyze the differences of various approaches of social network anonymization.
    Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
    2022-09-27 22:21