Recently, Professor Shafi Goldwasser, a co-founder of Duality Technologies, a Turing award and two-time Gödel Prize winner, interviewed Professor Daniel J. Weitzner, to discuss Internet privacy and public policy. Weitzner is the Director of the MIT Internet Policy Research Initiative, principal research scientist at the Computer Science and Artificial Intelligence Lab CSAIL, and professor of Internet public policy in MIT’s Computer Science Department.
All about Synthetic Data – advantages, disadvantages, and real-world use cases.
Criteria to consider when choosing an open-source homomorphic encryption library, and a review of libraries available in 2022.
Data has three basic states: at rest, in transit, and in use. Homomorphic Encryption allows computations to be performed on encrypted data while in use – without ever decrypting it – keeping data secure in all three states.
The answer to the privacy/AI conflict is privacy-preserving machine learning (PPML) – a step-by-step process to allow ML models to be trained without revealing or decrypting the data inputs.
Today’s data driven enterprise must harness the power of data and realize its value. However, in many cases, organizations are limited due to the complexity of their data footprint, compliance, and privacy regulation factors, as well as competitive, business policy, and budget assignment concerns.