all AI news
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption. (arXiv:2208.02587v1 [cs.LG])
Aug. 5, 2022, 1:10 a.m. | Syed Imtiaz Ahamed, Vadlamani Ravi
cs.LG updates on arXiv.org arxiv.org
The Machine Learning and Deep Learning Models require a lot of data for the
training process, and in some scenarios, there might be some sensitive data,
such as customer information involved, which the organizations might be
hesitant to outsource for model building. Some of the privacy-preserving
techniques such as Differential Privacy, Homomorphic Encryption, and Secure
Multi-Party Computation can be integrated with different Machine Learning and
Deep Learning algorithms to provide security to the data as well as the model.
In …
arxiv encryption homomorphic encryption learning lg machine privacy
More from arxiv.org / cs.LG updates on arXiv.org
Testing the Segment Anything Model on radiology data
1 day, 6 hours ago |
arxiv.org
Calorimeter shower superresolution
1 day, 6 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US