The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Privacy Preservation of Image Data With Machine Learning
|
|
Author(s): Chhaya Suryabhan Dule (Dayananda Sagar University, India)and Rajasekharaiah K. M. (AMC College of Engineering, Visvesvaraya Technological University, India)
Copyright: 2022
Pages: 27
Source title:
Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
Source Author(s)/Editor(s): Victor Lobo (NOVA Information Management School (NOVA-IMS), NOVA University Lisbon, Portugal & Portuguese Naval Academy, Portugal)and Anacleto Correia (CINAV, Portuguese Naval Academy, Portugal)
DOI: 10.4018/978-1-7998-9430-8.ch010
Purchase
|
Abstract
The methods used to predict, categorize, and recognize complex data like pictures, audio, and texts have been popular in machine learning. These methods are the basis for future AI-driven internet providers because of unparalleled precision in deep learning methodologies. Commercial firms gather large-scale user data and perform machine learning technique. The massive information necessary for machine learning raises privacy problems. The user's personal and extremely sensitive data such as photographs and voice records are gathered and retained forever by these commercial firms and users can not limit the intents of these sensitive information. In addition, centrally stored data is susceptible to legal and extrajudicial monitoring. Many data owners use profound extensive learning by security and confidentiality. This chapter contains a practical approach that allows several parties to learn a precise model of complex systems for a specific purpose without disclosing their data sets. It provides an interesting element in utility and privacy.
Related Content
|
G. Boopathy, Balaji Ganesan, P. Sivaprakasam, T. Kumaran.
© 2026.
42 pages.
|
|
G. Prasad.
© 2026.
14 pages.
|
|
Kishorebabu Dasari, Sujana Parry, Srinivas Mekala.
© 2026.
30 pages.
|
|
Chikesh Ranjan, Jonnalagadda Srinivas, P. S. Balaji, Kaushik Kumar.
© 2026.
24 pages.
|
|
G. Ananthi, S. Mehala Shevani, P. Priyadharshini Devi.
© 2026.
24 pages.
|
|
G. Prasad, Snehal Malik, Aadya Gupta, Yash Nigam.
© 2026.
26 pages.
|
|
Dhirendra Patel, M. L. Azad.
© 2026.
36 pages.
|
|
|