Public-Key Cryptosystems Based on Composite Degree Residuosity Classes

By Pascal Paillier. 1999

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In Privacy & Security

Citations in the corpus

Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, background): “Note that there exist relevant solutions based on homomorphic encryption (HE) [17, 18, 32], where the server computes the sum of encrypted inputs under a key shared among the clients.”
Secure Federated Submodel Learning (2019, background): “These basic settings make the secure aggregation protocol different from other relevant work about oblivious addition [19]–[21], or, more generally, secure multiparty computation [63]–[66], which requires direct peer-to-peer communication between clients; assumes the availability of multiple noncoll”
Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “With a different homomorphic encryption scheme, such as Paillier [61] or elliptic-curve-based El Gamal (ECEG), the MPCs would take longer, but the ciphertexts would be smaller.”
SecureBoost: A Lossless Federated Learning Framework (2019, methodology): “Next, we show how to obtain g l and h l with encrypted g i and h i using additively homomorphic encryption scheme (Paillier 1999).”
Secure Federated Transfer Learning (2018, methodology): “In this paper, we propose a possible solution to these challenges: Federated Transfer Learning (FTL), which leverages transfer learning technique (Pan et al. 2010) to provide solutions for the entire sample and feature space under a federation.”
A Review of Homomorphic Encryption Libraries for Secure Computation (2018, background): “Partially Homomorphic Encryption (PHE) schemes such as [9,10,11,12,13,14,15,16,17,18,19] allow only one type of operation any number of times - either addition or multiplication on encrypted data as compared to Somewhat Homomorphic schemes that support both.”
Private Stateful Information Retrieval (2018, methodology): “We construct a library that constructs PIR based on the Paillier cryptosystem [43] and build a PSIR scheme on top of the Paillier PIR library.”
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, methodology): “Additively homomorphic encryption — We recall here the main properties of additively homomorphic encryption schemes such as Paillier [1999]. As a public key system, any party can encrypt their data with a known public key and perform computations with data encrypted by others with the same public ke”
XPIR : Private Information Retrieval for Everyone (2016, background): “Parameters are dependent on each cryptographic system: ** NoCryptography if a trivial full database download is to be done after which PIRClient stores only the element the user is interested in. ** Paillier:A:B:C if Paillier’s cryptosystem is to be used with A security bits, a plaintext modulus of ”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “Paillier [44] presented an additively homomorphic cryptosystem that computes the encrypted sum using only encrypted values.”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, methodology): “Users submit their data encrypted under a linearly homomorphic encryption system such as Paillier [4] or Regev [5].”
I-Pic: A Platform for Privacy-Compliant Image Capture (2016, methodology): “We use the protocol described in [8], which is based on the Paillier homomorphic encryption scheme [19].”
CryptoDL: Deep Neural Networks over Encrypted Data (2017, background): “Other researchers followed to introduce several other HE schemes [22].”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, methodology): “An example of an additively homomorphic encryption (AHE) scheme is the Paillier scheme [28].”
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, methodology): “The Damg̊ard-Jurik cryptosystem (13) is a generalization of the Paillier cryptosystem which encrypts messages from the range [1, Ns] using computations modulo Ns+1, where N is an RSA modulus and s a natural number.”
Scalable and Private Media Consumption with Popcorn (2016, methodology): “When n (as will be the case in our context) and |c| is a small constant (e.g., 2048 in many Paillier implementations), |c| · n is much smaller than . use more than one server (k > 1), and assume that they do not collude; thus, in practice, the servers must belong to different administrative domains.”
Privacy-Preserving Statistical Analysis by Exact Logistic Regression (2015, methodology): “All cryptographic operations are conducted using Paillier’s semi-homomorphically additive cryptosystem [19].”