Name
Abstract | ||
|---|---|---|
Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and make inference on urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we adopt is an element of-differential-privacy in Sparse MCS to provide a theoretical guarantee for participants' location privacy regardless of an adversary's prior knowledge. Furthermore, to reduce the data quality loss caused by differential location obfuscation, we propose a privacy-preserving framework with three components. First, we learn a data adjustment function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an optimal location obfuscation function, which aims to minimize the uncertainty in data adjustment. We also propose a fast approximated variant. Third, we propose an uncertainty-aware inference algorithm to improve the inference accuracy of obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to existing differential privacy methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICDM.2016.41 | 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) |
Field | DocType | ISSN |
Data mining,Data quality,Differential privacy,Computer science,Inference,Server,Linear programming,Artificial intelligence,Information privacy,Obfuscation,Mobile telephony,Machine learning | Conference | 1550-4786 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leye Wang | 1 | 551 | 36.79 |
| Daqing Zhang | 2 | 3619 | 217.31 |
| Dingqi Yang | 3 | 542 | 28.79 |
| Brian Y. Lim | 4 | 327 | 23.95 |
| Xiaojuan Ma | 5 | 325 | 49.27 |