Cloud-enabled privacy-preserving collaborative learning for mobile sensing

By Bin Liu, Yurong Jiang, Fei Sha, Ramesh Govindan. 2012

107 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

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

1. Prateek Mittal
2. Aleksandra Korolova
3. Ú. Erlingsson
4. Supriyo Chakraborty
5. Changchang Liu
6. Vasyl Pihur
7. Peter Kairouz
8. Ninghui Li

In Computer Networks and Communications

1. Prateek Mittal
2. Supriyo Chakraborty
3. Changchang Liu
4. Ú. Erlingsson
5. Aleksandra Korolova
6. Neil Zhenqiang Gong
7. Binghui Wang

In Computer Science Applications

1. Ú. Erlingsson
2. Aleksandra Korolova
3. Vasyl Pihur
4. Prateek Mittal
5. Supriyo Chakraborty
6. Changchang Liu
7. Ninghui Li
8. Tianhao Wang

Citations in the corpus

• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response (2014, background): “Usage of various dimensionality reduction techniques in order to improve the privacy properties of algorithms while retaining utility is also fairly common [20, 17, 1, 22].”
• DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “But the same data can also be used to recognize the user’s identity (sensitive inference) [52], [66], compromising her privacy.”