Private and Dynamic Time-Series Data Aggregation with Trust Relaxation
By Iraklis Leontiadis, Kaoutar Elkhiyaoui, Refik Molva. 2014
47 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
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
- Amir Salman Avestimehr
- Jinhyun So
- Basak Guler
- H. Brendan McMahan
- Jakub Konecný
- Adrià Gascón
- Daniel Ramage
- Peter Kairouz
In Computer Networks and Communications
- Jinhyun So
- H. Brendan McMahan
- Adrià Gascón
- Shantanu D. Rane
- Mariana Raykova
- Refik Molva
- Tancrède Lepoint
- Daniel Ramage
Citations in the corpus
- Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, background): “While there have been both theoretical and appliedworks proposing secure computation solutions for settings with restricted communication [10, 14, 22, 26, 30, 33], Bonawitz et al.”
- Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, background): “Homomorphic encryption is a cryptographic secure computation scheme that allows aggregations to be performed on encrypted data [22], [23], [24].”
- Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, background): “the Paillier cryptosystem [40, 49]) can handle client dropouts, but are either computationally expensive or require additional trust assumptions.”
- Secure Aggregation with Heterogeneous Quantization in Federated Learning (2020, background): “In particular, secure aggregation can be done by using cryptographic approaches such as homomorphic encryption that allows aggregations to be performed on encrypted data [13], [14].”
- PUDA - Privacy and Unforgeability for Data Aggregation (2015, background): “[11,13,17]) so as to capture the fact that Aggregator A not only has access to ciphertexts ci,t, but also has access to the authentication tags σi,t that enable Aggregator A to generate proofs of correct aggregation.”
- Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks (2017, background): “Specifically, although the prior art could also be extended to compute linear combinations, in most cases ([1, 3, 4, 9, 12, 15, 16, 22]), the protocols have to reveal the weights ci to the participants.”