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What is Fully Homomorphic Encryption? Tying this back to the central topic of this article, fully homomorphic encryption (FHE) is a specific data encryption method that enables people to perform computations on encrypted data without revealing the raw data. Theoretically, the analysis and calculations performed on the encrypted data should produce results identical to those performed on the raw data. With FHE, we establish a 1:1 connection between data in the encrypted data set corresponding to data in the raw data set. The core component preservation, in this case, is the ability to perform any calculation on the data of either set and yield the same results.

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For context, many companies already take preventative measures to protect user data and maintain differential privacy. Companies rarely store data on the cloud or in their databases in raw, unencrypted form. Therefore, even if attackers gain control of a company’s servers, they still have to bypass the encryption to read and access the data. However, data is not interesting when it’s just sitting there, encrypted and unused. When companies want to perform analyses on data to derive valuable insights, they have no great option but to decrypt the data to do this. When decrypted, the data becomes vulnerable. However, through end-to-end encryption, FHE becomes very useful as we no longer have to decrypt data to analyze it; this is just scratching the surface of what is possible.

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Zama is company in the blockchain industry that is building open-source homomorphic encryption tools that developers can leverage to build exciting applications using FHE, blockchain, and AI. Zama has built a Fully Homomorphic Ethereum Virtual Machine (fhEVM) as part of its product offerings. This smart contract protocol enables on-chain transaction data to remain encrypted during processing. Developers exploring various applications with Zama’s library have been impressed with the performance, even in complex use cases. Zama successfully closed its $42 million Series A funding round in February 2022, led by Protocol Labs, elevating its total capital raised to $50 million. Visit : https://www.zama.ai/

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A crucial consideration is whether companies should be allowed to read and store our personal information to begin with. The standard response to this by many has been that companies need to see our data to provide us with better services. If YouTube doesn’t store data like my watch and search history, the algorithm can’t operate to its fullest potential and show me the videos I’m interested in. For this reason, many people have considered the tradeoff between data privacy and getting better services worth making. However, with FHE, we no longer need to make this tradeoff. Companies like YouTube can train their algorithms on encrypted data and produce identical results for the end user without infringing on data privacy. Specifically, they can homomorphically encrypt information such as my watch and search history, analyze it without looking at it since it is encrypted, and then show me the videos I’m interested in based on the analysis.

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