A Scalable Scheme for Privacy-Preserving Aggregation of Time-Series Data
By Marc Joye, Benoît Libert. 2013
153 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Robert Huijie Deng
- Zheng Yan
- Wenxiu Ding
- Henry Corrigan-Gibbs
- Andreas Haeberlen
- Dan Boneh
- Daniel Noble
- Edo Roth
In Computer Vision and Pattern Recognition
- Dan Boneh
- Henry Corrigan-Gibbs
- Shantanu D. Rane
- Zheng Yan
- Robert Huijie Deng
- Andreas Haeberlen
- Refik Molva
- Francesco Aldà
Citations in the corpus
- Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “Additional theoretical contributions have also operated in the local setting, but have included additional cryptographic tools [19, 46, 70].”
- Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “The servers can sum up the encrypted bits and decrypt only the final sum [48, 56, 81, 92, 99, 100].”
- Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks (2017, methodology): “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.”
- PUDA - Privacy and Unforgeability for Data Aggregation (2015, methodology): “[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.”
- Privacy-Preserving Data Processing with Flexible Access Control (2020, unknown): “Besides those schemes focusing on data aggregation [19], [20], [21], [22], [23], [24], [25], other studies were conducted to achieve more efficient privacy-preserving operations.”
- Computational Differential Privacy from Lattice-Based Cryptography (2017, methodology): “In a subsequent work [7], a generalisation of the scheme from [21] is obtained based on smooth projective hash functions (see [12]).”
- Encrypted data processing with Homomorphic Re-Encryption (2017, background): “Joye and Libert [16] proposed a practical scheme that can accommodate large plaintext spaces.”