A post-Covid era has boomed remote working environments, carved the way for hybrid work and shown many ways in which privacy can be compromised. So with enormous amounts of data and equipment being held and operated off-site and the migration to cloud applications, how are companies keeping their data safe?
Enter privacy-enhancing computation. The strategic technology trend appears on the annual report again this year.
What you need to know about privacy-enhancing computation?
Privacy-enhancing computation protects data in use while maintaining privacy or secrecy online common data-at-rest security measures. According to Gartner, by 2025, 50% of large organizations will implement privacy-enhancing computation to process data in untrusted environments and multiparty data analytics use cases.
With the maturing of privacy compliance and more widespread regulations, both small and large businesses will have to protect data in use.
According to Gartner, this type of security comes in three forms; the first could involve providing a trusted environment in which data can be processed or analysed through third-party and hardware-trusted execution environments.
The second type of privacy-enhancing computation concerns decentralised processing and analytics through federated or privacy-aware machine learning.
The final form concerns computation that transforms data and algorithms before processing or analytics, including zero-knowledge proof, secure multiparty computation and homomorphic encryption.
Homomorphic encryption (HE) uses cryptographic techniques to enable third parties to process encrypted data and return an encrypted result to the data owner while providing no knowledge about the data or the outcome. In practice, this type of encryption is not fast enough for business implementations.