Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Private data analytics on biomedical sensing data via distributed computation
Gong Y., Fang Y., Guo Y. IEEE/ACM Transactions on Computational Biology and Bioinformatics13 (3):431-444,2016.Type:Article
Date Reviewed: Jun 2 2017

Predictive model training systems actually lack positive samples such as biomedical data from healthy people due to data privacy preservation issues that mHealth users face. This paper therefore presents a novel approach that preserves data privacy with the objective of incentivizing mHealth users to confidently release their biomedical data. It focuses on logistic regression, a machine learning method, and its limitations regarding data privacy preservation.

The proposed privacy scheme has been evaluated using a real-world dataset. It has good performance and ensures high privacy-preserving capability. The error rates of the models that it computes are constant, while those computed by the local approach increase as the user number increases. This approach is more secure against collusion than the native approach, a summation protocol that works as a ring in which an initiator starts the summation process. The approach uses a homomorphic approach, which is “robust against collusion attacks and ... efficient for computation over multiple iterations” and anonymized data. Test results show that the proposed “computation protocol leaks no information beyond the intermediate and final aggregated regression parameters” and information leakage during the interactions is bounded. This provided data privacy preservation scheme would surely increase user confidence in mHealth applications and thus increase the provision of positive samples for predictive model training.

The alternating direction method of multipliers (ADMM) and its limitations are discussed. Functions are formulated, though not deeply described. However, parameter descriptions are missing in the functions’ where clause. Also missing is a comparison of the distributed approaches using vertically and horizontally partitioned data with each other, as well as a comparison of private predictive model training algorithms with each other.

Aside from these weaknesses, the paper is an excellent reference for researchers working on data privacy. The authors clearly describe their concept, evaluate their approach, and point out its novelty.

Reviewer:  Thierry Edoh Review #: CR145324 (1708-0540)
Bookmark and Share
  Reviewer Selected
Featured Reviewer
 
 
Sensor Networks (C.2.1 ... )
 
 
Health (J.3 ... )
 
 
Sensors (I.2.9 ... )
 
 
Data Encryption (E.3 )
 
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