Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Challenges of privacy-preserving machine learning in IoT
Zheng M., Xu D., Jiang L., Gu C., Tan R., Cheng P.  AIChallengeIoT 2019 (Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, New York, NY, Nov 10-13, 2019)1-7.2019.Type:Proceedings
Date Reviewed: Feb 3 2020

The widespread adoption of Internet of Things (IoT) devices has made available a huge amount of data, thus enabling the development of applications that use machine learning (ML) to gather and dig up information, even from a user’s private life (such as sensors over the body). However, there are two major privacy preservation issues when using data to train and infer from ML: the small amount of computational resources in the devices, and the communication channels with the cloud infrastructure, which can have delays and may be intermittent. These two factors influence the architecture--should computation be done in the devices or in the cloud? And what about sensitive data carried by the devices? In fact, privacy preservation poses a dilemma: should user data be obfuscated before leaving the devices or in the cloud?

The authors provide a taxonomy of the existing privacy-preserving approaches, and for each technique they cover eventual attacks, shortcomings, and limitations. They also present ObfNet, a ML algorithm for preserving privacy at the initial stage.

This topic is of utmost importance nowadays. In fact, IoT devices (and even medical devices) share user data for secondary usage. Although regulations such as the General Data Protection Regulation (GDPR) provide a legal framework to protect users, proactive and “by design” privacy preservation is required.

Technical readers and IoT system architects will benefit from both the literature survey and the proposed algorithms.

Reviewer:  Massimiliano Masi Review #: CR146868 (2006-0134)
Bookmark and Share
  Featured Reviewer  
 
Sensor Networks (C.2.1 ... )
 
 
Real-Time And Embedded Systems (C.3 ... )
 
 
Security and Protection (C.2.0 ... )
 
 
Security and Protection (D.4.6 )
 
Would you recommend this review?
yes
no
Other reviews under "Sensor Networks": Date
Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks
Nayebi A., Sarbazi-Azad H., Karlsson G. Journal of Systems and Software 83(8): 1310-1317, 2010. Type: Article
Nov 8 2010
Heartbeat of a nest: using imagers as biological sensors
Ko T., Ahmadian S., Hicks J., Rahimi M., Estrin D., Soatto S., Coe S., Hamilton M. ACM Transactions on Sensor Networks 6(3): 1-31, 2010. Type: Article
Jan 10 2011
Efficient clustering-based data aggregation techniques for wireless sensor networks
Jung W., Lim K., Ko Y., Park S. Wireless Networks 17(5): 1387-1400, 2011. Type: Article
May 8 2012
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy