IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy
View Sample PDF
Author(s): Anitha J. (Dayananda Sagar Academy of Technology and Management, India)and Prasad S. P. (Dayananda Sagar Academy of Technology and Management, India)
Copyright: 2021
Pages: 17
Source title: Research Anthology on Privatizing and Securing Data
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8954-0.ch015

Purchase

View A Secure Protocol for High-Dimensional Big Data Providing Data Privacy on the publisher's website for pricing and purchasing information.

Abstract

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

Related Content

Chaymaâ Boutahiri, Ayoub Nouaiti, Aziz Bouazi, Abdallah Marhraoui Hsaini. © 2024. 14 pages.
Imane Cheikh, Khaoula Oulidi Omali, Mohammed Nabil Kabbaj, Mohammed Benbrahim. © 2024. 30 pages.
Tahiri Omar, Herrou Brahim, Sekkat Souhail, Khadiri Hassan. © 2024. 19 pages.
Sekkat Souhail, Ibtissam El Hassani, Anass Cherrafi. © 2024. 14 pages.
Meryeme Bououchma, Brahim Herrou. © 2024. 14 pages.
Touria Jdid, Idriss Chana, Aziz Bouazi, Mohammed Nabil Kabbaj, Mohammed Benbrahim. © 2024. 16 pages.
Houda Bentarki, Abdelkader Makhoute, Tőkési Karoly. © 2024. 10 pages.
Body Bottom