Verifiable differential privacy
By Arjun Narayan, Ariel J. Feldman, Antonis Papadimitriou, Andreas Haeberlen. 2015
47 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Public Health, Environmental and Occupational Health, 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
- Fernando Perez-Cruz
- Giuseppe Ateniese
- Briland Hitaj
- Arjun Narayan
- Emiliano De Cristofaro
- Luca Melis
- Vitaly Shmatikov
- Congzheng Song
In Public Health, Environmental and Occupational Health
In Privacy & Security
- Fernando Perez-Cruz
- Giuseppe Ateniese
- Briland Hitaj
- Arjun Narayan
- Emiliano De Cristofaro
- Luca Melis
- Vitaly Shmatikov
- Congzheng Song
Citations in the corpus
- Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (2017, background): “Several works have adopted DP as an efficient defense mechanism such as [5, 7, 11, 12, 18, 23, 24, 37, 53, 60, 65, 72, 86].”
- Distributed Differential Privacy and Applications (2015, methodology): “For example, Narayanan and Shmatikov (2008) deanonymized the anonymized Netflix dataset by linking it to the public IMDB dataset, as many viewers had rated some movies on both datasets.”