Literature Studies performed by MSc students In the context of course  Literature study and Seminar

​

​​

1. Modern Data Engineering, 2024 [pdf]

2. Cloud-Edge computing 2024 [pdf]

3. Enforcing software policies, 2023 [pdf]

4. Technical Challenges and Opportunities in Explainable Artificia Intelligence: A Survey, 2023 [pdf]

5. Personalized cancer vaccine design: A Literature Review, 2023 [pdf]

6. Uncover the Secrets of PKCE: Elevating OAuth2.0 for security of native clients, 2023 [pdf]

7. Social Network Analysis Applied to Research Collaboration, 2023 [pdf]

8. Performance Modeling of Spark: An overview, 2023 [pdf]

9. Literature Research: Function as a Service and Serverless Computing in the Cloud industry, 2022 [pdf]

10. A Review of AI-based Resource Allocation Approaches in Cloud Environments, 2022 [pdf]

11. The Semantic Web Status: A Literature Review, 2022 [pdf]

12. Event Stream Processing: A Literature Review [pdf]

13. Collaborative Machine Learning-Driven Internet of Medical Things - A Systematic Literature Review [pdf]

14. Privacy Attacks Against Generative Models, 2022 [pdf]

15. Survey on Privacy Protection in Federated Learning, 2022 [pdf]

16. Machine Learning in Production: A Literature Review, 2021 [pdf]  

17. Privacy Preserving Machine Learning-Based Methods for Synthetic Data Generation: A Survey and Review. 2020 [pdf]

18. Inference and Extraction Attacks on Machine Learning Models: A Review 2020 [pdf]

19. MLOps and data versioning in machine learning project, 2020 [pdf]

20. Protocol for a Systematic Literature Review on Scaling up Machine Learning,2019 [pdf]

21. Technical Analysis of the Blockchain and the Cryptocurrency Market, 2018 [pdf]

22. Distributed file systems: a current overview and future outlook, 2018 [pdf]

23. Protocol for a Systematic literature review on distributed data management in Machine learning systems, 2018 [pdf]

24. Spark: Past, Present and Future, 2017 [pdf]

25. Virtual machine and Docker container: a different approach to Virtualization,2017 [pdf]

26. Battle of the big data,2016  [pdf]

27. Literature_Studies_Course_Web_Service_and_Cloud_Systems_2019-2020 [pdf]

28. Literature_studies_Course_Web_Service_and_Cloud_Systems_2020-2021 [pdf​]

29. Literature_studies_Course_Web_Service_and_Cloud_Systems_2021-2022 [pdf​]

​

Topics and literature study performed in the context of the course Literature study and Seminar

​

Topics for a literature study  

​

 - Topic 1: event-based systems and event sourcing.

•  starting point:  

1.    https://www.slideshare.net/GlobalLogicUkraine/event-sourcing-and-functional-programming

2.    https://www.kenneth-truyers.net/2013/12/05/introduction-to-domain-driven-design-cqrs-and-event-sourcing/

 

- Topic 2: Privacy-preserving machine learning/private AI by leveraging blockchain

•   starting points

  1. Kuo, Tsung-Ting, and Lucila Ohno-Machado. "Modelchain: Decentralized privacy-preserving healthcare predictive modelling framework on private blockchain networks." arXiv preprint arXiv:1802.01746 (2018).

  2. Kim, Hyunil, et al. "Efficient privacy-preserving machine learning for blockchain network." IEEE Access 7 (2019): 136481-136495.

    

- Topic 3: Privacy-preserving data publication

•   starting points

  1. Chen, Rui, et al. "Correlated network data publication via differential privacy." The VLDB Journal—The International Journal on Very Large Data Bases 23.4 (2014): 653-676.

  2. Ren, Xuebin, et al. "$\textsf {LoPub} $: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy." IEEE Transactions on Information Forensics and Security 13.9 (2018): 2151-2166.

  3. Li, Xiang-Yang, et al. "Graph-based privacy-preserving data publication." IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 2016.

​

​

- Topic 4: Membership inference and other unmasking attacks on ML models

•   starting points

  1.  Shokri, Reza, et al. "Membership inference attacks against machine learning models." 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 2017.

  2. Rahman, Md Atiqur, et al. "Membership Inference Attack against Differentially Private Deep Learning Model." Transactions on Data Privacy 11.1 (2018): 61-79

  3.  Salem, Ahmed, et al. "Ml-leaks: Model and data-independent membership inference attacks and defences on machine learning models." arXiv preprint arXiv:1806.01246 (2018).

 

- Topic 5: Privacy issues about ML in healthcare

•   starting points

  1.   Mooney, Stephen J., and Vikas Pejaver. "Big data in public health: terminology, machine learning, and privacy." Annual review of public health 39 (2018): 95-112.

  2. Horvitz, Eric, and Deirdre Mulligan. "Data, privacy, and the greater good." Science 349.6245 (2015): 253-255.

  3. Shakeel, P. Mohamed, et al. "Maintaining security and privacy in the health care system using learning-based deep-Q-networks." Journal of medical systems 42.10 (2018): 186.

​

- Topic 6: Distributed/collaborative/multi-party machine learning in healthcare

•   starting points

  1.  starting pointsRongjun, X. I. E., et al. "Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare." Computer Networks 149 (2019): 127-143.

  2. Farahani, Bahar, Mojtaba Barzegari, and Fereidoon Shams Aliee. "Towards Collaborative Machine Learning-Driven Healthcare Internet of Things." Proceedings of the International Conference on Omni-Layer Intelligent Systems. ACM, 2019.

​

​

 

1.