PrivCheck: privacy-preserving check-in data publishing for personalized location based services
By Dingqi Yang, Daqing Zhang, Bingqing Qu, Philippe Cudré-Mauroux. 2016
65 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science
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
- Congzheng Song
- Vitaly Shmatikov
- Qiang Yang
- Emiliano De Cristofaro
- Luca Melis
- Reza Shokri
- Jun Zhou
- Chaochao Chen
In Computer Science Applications
In Sociology and Political Science
- Vitaly Shmatikov
- Congzheng Song
- Yuan Qi
- Luca Melis
- Qiang Yang
- Xiaolin Zheng
- Chaochao Chen
- Emiliano De Cristofaro
Citations in the corpus
- Exploiting Unintended Feature Leakage in Collaborative Learning (2018, methodology): “For our experiments, we selected a subset of 15,548 users who checked in at least 10 different locations in New York City and for whom we know their gender [65].”
- Secure Federated Matrix Factorization (2019, background): “, age, relationship status, political views, and sex orientations [9].”
- Inference Attacks Against Collaborative Learning (2018, methodology): “For our experiments, we selected a subset of 15,548 users who checked in at least 10 different locations in New York City and for whom we know their gender [56].”
- Practical Privacy Preserving POI Recommendation (2020, background): “First, Foursquare is a famous benchmark dataset for POI recommendation [60].”