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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:88181 |
| Date | 21 November 2023 |
| Creators | Löchner, Marc |
| Contributors | Burghardt, Dirk, Mäs, Stephan, Funke, Stefan, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.3390/ijgi9120709, 10.3390/ijgi12020060 |
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