Name
Abstract | ||
|---|---|---|
Mobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability that a particular individual cannot be identified when mobility data is outsourced to third parties for analysis. However, challenges remain with the privacy of the queries on outsourced analysis results, especially when the queries are sent directly to third parties by end-users. Drawing inspiration from our earlier work in privacy preserving collaborative filtering (CF) and next location prediction, in this exploratory work, we propose a novel representation of trajectory data in the CF domain and experiment with a privacy preserving Slope One CF predictor. We present evaluations for the accuracy and the computational performance of our proposal using anonymised data gathered from real traffic data in the Italian cities of Pisa and Milan. One use-case is a third-party location-prediction-as-a-service deployed on a public cloud, which can respond to privacy-preserving queries while enabling data owners to build a rich predictor on the cloud. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CloudCom.2014.114 | Cloud Computing Technology and Science |
Keywords | Field | DocType |
cloud computing,collaborative filtering,data analysis,data mining,data privacy,mobile computing,probability,query processing,traffic engineering computing,CF-inspired privacy-preserving next location prediction,Italian cities,LBS,Milan,Pisa,anonymisation mechanisms,data owners,location sensors,location-based services,mobility data,outsourced analysis,privacy preserving Slope One CF predictor,privacy preserving collaborative filtering,privacy preserving data analysis,probability,public cloud,query privacy,real traffic data,spatio-temporal data mining,third-party location-prediction-as-a-service,trajectory data representation,collaborative filtering,location,prediction,privacy | Data mining,Collaborative filtering,Slope One,Information retrieval,Cryptography,Computer science,Global Positioning System,Information privacy,Pound (mass),Privacy software,Cloud computing,Distributed computing | Conference |
ISSN | Citations | PageRank |
2330-2194 | 1 | 0.36 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| anirban basu | 1 | 138 | 12.81 |
| Corena, J.C. | 2 | 1 | 0.36 |
| A. Monreale | 3 | 36 | 2.27 |
| Dino Pedreschi | 4 | 3083 | 244.47 |