The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures
Abstract
Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.
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.
|
|
|