While research has been conducted on social media, few comparisons have been made in regards to the privacy issues that exist within the most common social media networks, such as Facebook, Google Plus, and Twitter. Most research has concentrated on technical issues with the networks and on the effects of social media in fields such as medicine, law, and science. Although the effects on these fields are beneficial to the people related to them, few studies have shown how everyday users are affected by the use of social media. Social media networks affect the privacy of users because the networks control what happens to user contact information, posts, and other delicate disclosures that users make on those networks. Social media networks also have the ability to sync with phone and tablet applications. Because the use of these applications requires additional contact information from users, social media networks are entrusted with keeping user information secure. This paper analyzes newspaper articles, magazine articles, and research papers pertaining to social media to determine what effects social media has on the user's privacy and how much trust should be placed in social media networks such as Facebook. It provides a comprehensive view of the most used social media networks in 2012 and offers methods and suggestions for users to help protect themselves against privacy invasion.

Social media data is used for analytics, e.g., in science, authorities or the industry. Privacy is often considered a secondary problem. However, protecting the privacy of social media users is demanded by laws and ethics. In order to prevent subsequent abuse, theft or public exposure of collected datasets, privacy-aware data processing is crucial. This dissertation presents a concept to process social media data with social media user’s privacy in mind. It features a data storage concept based on the cardinality estimator HyperLogLog to store social media data, so that it is not possible to extract individual items from it, but only to estimate the cardinality of items within a certain set, plus running set operations over multiple sets to extend analytical ranges. Applying this method requires to define the scope of the result before even gathering the data. This prevents the data from being misused for other purposes at a later point in time and thus follows the privacy by design principles. This work further shows methods to increase privacy through the implementation of abstraction layers. An included case study demonstrates the presented methods to be suitable for application in the field.:1 Introduction 1.1 Problem 1.2 Research objectives 1.3 Document structure 2 Related work 2.1 The notion of privacy 2.2 Privacy by design 2.3 Differential privacy 2.4 Geoprivacy 2.5 Probabilistic Data Structures 3 Concept and methods 3.1 Collateral data 3.2 Disposable data 3.3 Cardinality estimation 3.4 Data precision 3.5 Extendability 3.6 Abstraction 3.7 Time consideration 4 Summary of publications 4.1 HyperLogLog Introduction 4.2 VOST Case Study 4.3 Real-time Streaming 4.4 Abstraction Layers 4.5 VGIscience Book Chapter 4.6 Supplementary Software Materials 5 Discussion 5.1 Prevent accidental data disclosure 5.2 Feasibility in the field 5.3 Adjustability for different use cases 5.4 Limitations of HLL 5.5 Security 5.6 Outlook and further research 6 Conclusion Appendix References Publications