Date of Award
7-7-2020
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Deep Learning, Differential Privacy, Privacy-preserving
Supervisor
Saeed Samet
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
With recent technological advancements, the amount of personal user data that is being generated is immense. Due to the large volume of data, machine learning algorithms such as neural networks are serving as the backbone to derive patterns from this data quickly. This need for big data analytics comes at the cost of the privacy of user data. The second challenge that must be solved relates to the scalability of the machine learning algorithm. Neural networks are known to deteriorate as the volume of the data increases due to complex sum and sigmoid calculations. Therefore in this thesis, an attempt to parallelize the neural network while also maintaining the privacy of user data is made. This model would provide a viable option for big data analytics without sacrificing the privacy of individual users while also maintaining precision and the classification accuracy of the model. The implementation of the parallelized privacy preserving neural network will be based on the MapReduce computing model which provides advanced features such as fault tolerance, data replication, and load balancing.
Recommended Citation
Patel, Dipeshkumar Shaileshkumar, "Parallel Implementation of Privacy Preserving Multi-Layer Neural Networks" (2020). Electronic Theses and Dissertations. 8386.
https://scholar.uwindsor.ca/etd/8386