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Homomorphic Encryption: Unlocking Privacy-Preserving Computation
- Homomorphic encryption allows you to keep your data hidden while still letting someone perform calculations on it.
- There are three types of homomorphic encryption, each suited to different use cases.
- Homomorphic encryption is particularly valuable in situations where data secrecy is essential, but there’s still a need to perform complex calculations or analysis on that data.
- While homomorphic encryption remains a technically advanced area, using libraries like Zama, Microsoft SEAL, or HElib significantly simplifies the process.
- Several open-source libraries and tools make implementing homomorphic encryption easier.
- Homomorphic encryption allows sensitive information to remain encrypted and hidden throughout the entire computation process, ensuring privacy at every stage.
- Microsoft SEAL, HElib, and PALISADE as well as Zama have made it easier to experiment with and adopt this technology.
- Homomorphic encryption is a powerful PET that allows organizations to perform computations on encrypted data, preserving privacy without sacrificing utility.
- Homomorphic encryption comes with several downsides including technical challenges, performance trade-offs and implementation complexity.
- Some homomorphic encryption schemes, such as CKKS, allow computations on encrypted real and complex numbers, making it well-suited for practical applications like AI and machine learning.
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