Yannick Schrade captures this new reality this way: Distributed privacy computing is the missing link in distributed s. With artificial intelligence models becoming increasingly important in decision making across industries, the ability to perform computations in a completely encrypted environment is undesirable.
Yannick Schrade captures this new reality this way: Distributed privacy computing is the missing link in distributed s. With artificial intelligence models becoming increasingly important in decision making across industries, the ability to perform computations in a completely encrypted environment is undesirable.
Schrade pointed to the potential of confidential artificial intelligence and confidential decentralized finance (DeFi) as examples of what such technologies could be: How to easily integrate strong privacy measures without sacrificing accessibility for users.
Companies that are pioneering privacy-enhancing technologies are operating in complex situations. As Schrade noted, traditional Web2 companies are well aware of the importance of privacy, and this is due to both regulatory requirements and business logic. In contrast, it is only recently that the #Web3 eco has begun to adopt privacy-centric solutions on a large scale.
While guaranteeing privacy is becoming unthinkable, it often comes at the expense of user experience. Because of the massive adoption of privacy computing, Schrade says, the end user should not notice that they are using privacy-oriented computing technology.
Efficiency, low latency and intuitive design are essential, especially in artificial intelligence-based applications where computing power requirements are high.
Shahaf Bar-Geffen, CEO of COTI, also spoke about the important role of artificial intelligence in shaping the future of privacy technologies.
Bar-Geffen highlighted the growing importance of federated learning, which allows organizations to train artificial intelligence models on distributed datasets without providing raw data.
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