Abstract
While the growing popularity of mobile devices has brought great convenience to the general public, it also put the privacy of the individuals at risk. Mobile users' data are being collected by mobile devices every day for business and research purposes. The use of data mining tools has become increasingly popular. As such, great care must be taken, as the data collected may contain sensitive personal information. While the data may not contain explicit identifiers, they include information about location, physical attributes, or even payment history of an individual. When combined with some publicly available information, these data could be linked to the individual. This chapter introduces k-anonymity and differential privacy, two models that are commonly used to capture privacy requirements. We also discuss the various mechanisms with respect to these two models.
Original language | English |
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Title of host publication | Mobile Security and Privacy |
Subtitle of host publication | Advances, Challenges and Future Research Directions |
Publisher | Elsevier Inc. |
Pages | 235-245 |
Number of pages | 11 |
ISBN (Electronic) | 9780128047460 |
ISBN (Print) | 9780128046296 |
DOIs | |
Publication status | Published - 13 Sept 2016 |
Keywords
- Data anonymity
- Data privacy
- Data sharing
- Differential privacy
- K-anonymity
- Privacy model
ASJC Scopus subject areas
- General Computer Science