Multi-Input Functional Encryption
By Shafi Goldwasser, Vipul Goyal, Abhishek Jain, Amit Sahai. IACR Cryptology ePrint Archive, 2014
343 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/2671a510c47b7fbe117fa07051829914cd1b4c98
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Gorbunov
- Ben Fisch
- Dhinakaran Vinayagamurthy
- Athanasios V. Vasilakos
- Zheng Yan
- Heiko Ludwig
- Nathalie Baracaldo
- Ali Anwar
In Computer Vision and Pattern Recognition
- Sergey Gorbunov
- Zheng Yan
- Athanasios V. Vasilakos
- Dan Boneh
- Brent Waters
- Craig Gentry
- Xavier Boyen
- Amit Sahai
Citations in the corpus
- IRON: Functional Encryption using Intel SGX (2017, background): “Functions can be deterministic or randomized with respect to the input in both single and multi-input settings [24, 28].”
- HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019, methodology): “rom the encrypted data,enc(x), without revealing the plaintext x. To satisfy the distributed setting, a variant of functional encryption, i.e., Multi-Input Functional Encryption (MIFE) is proposed in [20], where a functional secret key skf can correspond to an n-ary function f (·)that takes multiple”
- A Survey of Verifiable Computation (2017, background): “Moreover, Goldwasser et al. [88] pointed out that existing multi-input functional encryption schemes are based on non-falsifiable assumptions related to obfuscation that cannot be used.”