Results for tag: "Secure Multiparty Computation"

Secure multiparty computation (SMPC or MPC) is a sub-domain within the field of cryptography and computing that deals with methods enabling parties to jointly compute a function over their inputs while keeping those inputs private. The underlying premise is that the computing parties do not need to reveal their data to each other or a third party, thus ensuring data privacy and security. This approach is useful for scenarios where sharing data can be sensitive or regulated, such as in financial services, healthcare, and government operations. As computational capabilities and needs for data privacy have grown, the commercial interest in secure multiparty computation has expanded, prompting the development of specialized algorithms, software, and services that facilitate this kind of secure data collaboration. Several companies are active in the secure multiparty computation market, offering solutions that cater to privacy-preserving data analytics, federated learning, and secure data sharing. Notable players in this space include Unbound Tech, which provides security solutions based on MPC technology; Partisia Blockchain, a blockchain-based platform leveraging MPC for transparency and privacy; Sepior, which specializes in key management and protection using MPC; Sharemind, developed by Cybernetica, that offers secure data processing tools; and Multiparty Computation solutions from IBM, a leading enterprise in the field of advanced computing and cryptography. These companies, among others, are at the forefront of developing and commercializing technologies that enable secure, private computation across various industries. Show Less Read more