Privacy of location data

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Table of contents

1 Introduction
1.1 Personal data sharing in social networks
1.2 Current techniques to match accounts
1.3 Privacy and security threats in social networks
1.4 Contributions
1.4.1 Reliable and scalable account matching across social networks
1.4.2 Account matching by exploiting innocuous user activities
1.4.3 Detection and characterization of impersonators
1.5 Organization of the thesis
2 State of the Art 
2.1 Social networks
2.1.1 Measurement and characterization of social networks
2.1.2 Inferring additional information about users in social networks
2.1.3 Aggregating users’ data across social networks
2.2 Online privacy
2.2.1 Online tracking and advertising
2.2.2 Privacy of online services
2.2.3 Privacy of location data
2.2.4 Privacy implications of matching and exploiting users’ data
2.3 Matching entities across data sources
2.3.1 Entity resolution
2.3.2 Anonymization and de-anonymization of user identities
2.4 Matching accounts across social networks
2.4.1 Matching accounts using private user data
2.4.2 Matching accounts using public user data
2.5 Security threats in social networks
3 Account Matching Framework 
3.1 The matching problem: definition
3.2 The matching problem: challenges
3.3 The matching problem: scalability and reliability
3.3.1 The ACID test for scalable and reliable matching
3.4 Ground truth data
3.5 Evaluation method
3.5.1 Evaluation metrics
3.5.2 Evaluation over a small dataset
3.5.3 Evaluation at scale
3.5.4 Evaluation against human workers
3.6 Summary
4 Matching Accounts using Public Profile Attributes 
4.1 ACID test of public attributes
4.1.1 Attribute availability
4.1.2 Attribute consistency
4.1.3 Attribute discriminability
4.1.4 Attribute impersonability
4.2 One-step matching scheme
4.2.1 Design of the scheme
4.2.2 The Linker
4.2.3 Evaluation over a small dataset
4.2.4 Evaluation at scale
4.2.5 Evaluation against human workers
4.3 Three-step matching scheme
4.3.1 Design of the scheme
4.3.2 The Filter
4.3.3 The Disambiguator
4.3.4 The Guard
4.3.5 Evaluation at scale
4.3.6 Evaluation against human workers
4.4 Testing the reliability of the three-step matching scheme
4.4.1 Reliability in the absence of a matching account
4.4.2 Reliability to impersonation
4.5 Matching in the wild
4.6 Summary
5 Matching Accounts using Public Innocuous Information
5.1 Features of innocuous activity
5.2 Location fingerprint
5.2.1 Building the fingerprint
5.2.2 Similarity metrics
5.2.3 Evaluation at scale
5.2.4 Implications
5.3 Timing fingerprint
5.4 Language fingerprint
5.5 Combining features
5.5.1 Method
5.5.2 Evaluation at scale
5.5.3 Comparison with screen name matching
5.6 Discussion
5.7 Summary
6 Detection and Characterization of Impersonators
6.1 A framework to detect impersonating accounts
6.1.1 Problem definition and approach
6.1.2 Dataset
6.1.3 Features
6.2 Detection of accounts that portray the same person
6.2.1 Naive approach
6.2.2 Single-site matching scheme
6.2.3 Evaluation using AMT workers
6.3 Detection of impersonating accounts
6.3.1 Ground truth
6.3.2 Methods to detect impersonating accounts
6.3.3 Evaluation over unlabeled pairs of accounts
6.4 Detecting impersonating accounts using humans
6.5 Characterization of impersonation attacks
6.6 Summary
7 Conclusions and Perspectives 
7.1 Summary of contributions
7.2 Future work
7.2.1 Improving matching schemes
7.2.2 Applications of matching
7.2.3 Protecting user privacy