A correction to 'efficient and secure comparison for on-line auctions'

By Ivan Bjerre Damgård, M. Geisler, Mikkel Krøigaard. 2009

124 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, background): “There are protocols that calculate the maximum of two encrypted numbers (see, for example, [Damgård et al., 2008]) which could be used to calculate the maximum of encrypted exponents (needed during addition as seen above).”
Efficient Privacy-Preserving Face Recognition (2009, methodology): “In the most recent proposal for privacy-preserving face recognition [12] the authors use the standard and popular Eigenface [34,33] recognition algorithm and design a protocol that performs operations on encrypted images by means of homomorphic encryption schemes, more concretely, Pailler [29,11] as”
Semi-Homomorphic Encryption and Multiparty Computation (2011, methodology): “These include: the Okamoto-Uchiyama cryptosystem [OU98]; Paillier cryptosystem [Pai99] and its generalization by Damg˚ ard and Jurik [DJ01]; Regev’s LWE based cryptosystem [Reg05]; the scheme of Damg˚ ard, Geisler and Kroigaard [ DGK09 ] based on a subgroup-decision problem; the subset-sum based sch”
Privacy-Preserving Face Recognition (2009, methodology): “At the core of our protocol is a comparison protocol due to Damg˚ard, Geisler and Krøigaard [8,9].”
Improved Garbled Circuit Building Blocks and Applications to Auctions and Computing Minima (2009, methodology): “The auction system proposed in [14,15,16] extends the idea of splitting the computation of the auction function between two parties, the auctioneer (called”