Abstract
Attackers and their methodologies are getting creative. With the aid of growing technologies such as machine learning, meaningful information can be deduced from very little data. We show that attackers are able to utilize such promising technologies to perform side-channel attacks to obtain data that can identify individuals, their habits and interests, health information, and much more. Furthermore, we highlight that such data can be obtained in a legal and consensual context, making it very hard to determine if an individual's privacy has been breached or not.
| Original language | English |
|---|---|
| Pages (from-to) | 182-191 |
| Number of pages | 10 |
| Journal | International Journal of Development and Conflict |
| Volume | 12 |
| Issue number | 2 |
| State | Published - Dec 2022 |
Bibliographical note
Publisher Copyright:© 2022 Gokhale Institute of Politics and Economics. All rights reserved.
Keywords
- Deep Learning
- Invasion
- Machine Learning
- Privacy
- Side-Channel Attacks
ASJC Scopus subject areas
- Development
- Economics, Econometrics and Finance (miscellaneous)
- Political Science and International Relations