A Generalisation, a Simplification and Some Applications of Paillier's Probabilistic Public-Key System

By Ivan Bjerre Damgård, Mads Jurik. 2001

1,135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Privacy & Security

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

In Economics and Econometrics

In Privacy & Security

Citations in the corpus

A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “Details of key generation, encryption and decryption algorithms can be found in [18], [33].”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, neutral): “Instead of Regev's scheme, we suggest Paillier's scheme [4] or its generalization by Damgård and Jurik [17] · · · · · · · · · · · · cn x x2,y2 A2 = x2x T 2 b2 = x2y2 c2 = Epk csp (A2; b2) c 2 x x1,y1 A1 = x1x T 1 b1 = x1y1 c1 = Epk csp (A1; b1) c 1 Evaluator c = n i=1 ci ⊗ cλ skcsp CSP [on-line]…”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “There are several useful PHE examples [Rivest et al. 1978b; Goldwasser and Mi-cali 1982; ElGamal 1985; Benaloh 1994; Naccache and Stern 1998; Okamoto and Uchiyama 1998; Paillier 1999; Damgård and Jurik 2001; Kawachi et al. 2007] in the literature.”
Secure Multiparty Computation for Privacy-Preserving Data Mining (2009, neutral): “The Damg̊ard-Jurik cryptosystem (13) is a generalization of the Paillier cryptosystem which encrypts messages from the range [1, N] using computations modulo N, where N is an RSA modulus and s a natural number.”
A Fast Single Server Private Information Retrieval Protocol with Low Communication Cost (2014, neutral): “Lipmaa uses the Damgård-Jurik scheme [27].”
Communication-Computation Trade-offs in PIR (2019, methodology): “As expected, Damgård–Jurik and ElGamal are signiﬁcantly slower than MulPIR and Gentry–Ramzan.”
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019, methodology): “ SMC protocols are used to aggregate the global model (see Table 1). PySyft [34] employs the SPDZ protocol [12, 13]. Truex et al. [38] uses a threshold-based partially additive homomorphic encryption [11]. Bonawitz et al. [6] makes use of secret sharing that enables authenticated encryption. These a”
Practical multi-candidate election system (2001, background): “Both of these arguments are the ma~n components to design multi-candidate election schemes and finally a threshold version of this crypt@system appeared in [15] and was rediscovered independently but later in [11].”
Compressible FHE with Applications to PIR (2019, methodology): “But Damg˚ard-Jurik is only additively homomorphic, so their solution becomes much more complicated, relying on the Ishai-Paskin homomorphic construction for branching-programs [IP07] (which internally uses Damg˚ard-Jurik).”
Secure multiparty aggregation with differential privacy: a comparative study (2013, methodology): “The Paillier scheme is a probabilistic public-key encryption scheme [13, 26, 35], which works as follows.”