05496nam 2200361 450 991041235390332120230825112135.010.1145/3328778(CKB)5280000000243438(NjHacI)995280000000243438(EXLCZ)99528000000024343820230825d2020 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierProceedings of the 51st ACM Technical Symposium on Computer Science Education /Jian Zhang [and three others]New York, New York :Association for Computing Machinery,2020.1 online resource (1552 pages) illustrationsACM Conferences1-4503-6793-3 Welcome to the 51st SIGCSE Technical Symposium on Computer Science Education (the 2020 Symposium), the premiere technical conference for computing educators. The 2020 Symposium is sponsored by the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE). SIGCSE has the third largest membership of any of ACM's Special Interest Groups (SIG), and is among the oldest SIGs. Only ten SIGs were founded prior to 1968 when SIGCSE was formed. Last year, we marked the 50th anniversary of the Symposium. Only six SIGs have held conferences with 50+ iterations. We truly have a history to celebrate and we did celebrate with a series of events that honored our past and we are coming into 2020 looking forward to our next 50 years. One of the most exciting things about the Symposium is the fact that it continues to grow. I made a bold prediction last year that we will see 2020 attendance in 2020. Our submission rates for this year's conference broke records again. The conference organizing committee rose to the challenge of accommodating the clear demand from the community while maintaining the character of a conference that so many look forward to each year by adding a full session of papers, panels, and special sessions after the traditional Saturday lunch. As our community continues to grow, these challenges will continue to face our conference. The board and the conference organizing committees are continually looking for ways to incorporate growth while maintaining the character of our event. The Symposium is about the people, community, and a desire to become better computing educators and it is important to celebrate and honor that every year. Speaking of the SIGCSE Board, I would like to take a moment to remind everyone that this is the first conference for the new SIGCSE Board (2019-2022). All of the members of the current board are at the conference and are very interested in hearing what we as a board can do for the community during our term on the board. Please feel free to reach out during the conference or after with feedback to the board about the event or any other aspect of SIGCSE. As an attendee, it is often difficult to imagine the amount of time and effort needed to put together an event the size of the Symposium. There are countless hours, handling crises that arise (big and small), and coordinating a committee of nearly 100 volunteers that help shape the program and events of the conference. It truly is a dedication to the community and the conference that motivates Symposium chairs to do their job. While it may be my honor on behalf of the SIGCSE organization and Board to be the first to thank them for their hard work this past year, I would like to not be the last. Feel free to reach out to the conference co-chairs Jian Zhang and Mark Sherriff and program co-chairs Sarah Heckman, Pamela Cutter and Alvaro Monge and thank them as you see them over the next few days. Our conference provides us with a chance to honor two people for their contributions to computer science education and the SIGCSE community. The annual SIGCSE award for Outstanding Contribution to Computer Science Education will be given to Lauri Malmi (Aalto University/Helsinki University of Technology). Lauri is world leader in computer science education research focusing on automatic assessment and program and algorithmic visualization. For over 20 years, he has been producing high quality publications and has won several awards, most recently, the best paper award at ICER 2019. He has also supervised 17 iv computing education PhD students. Starting in his native Finland, Lauri has led initiatives to disseminate computing education tools and research among university faculty. However, his reach is much larger, including his work to expand the Koli Calling conference to be an international venue for computer science education research, his work with the Scandinavian Pedagogy of Programming network and his editorial board work (ACM Inroads, ACM TOCE, IEEE Transactions on Learning Technologies). Lauri was also co-chair of ICER 2016 and 2017 and helped to lead major changes to the structure and reviewing for the conference. Lauri has truly helped to shape the global computing education community.Electronic data processingStudy and teaching (Higher)Electronic data processingStudy and teaching (Higher)004Zhang Jian652044NjHacINjHaclBOOK9910412353903321Proceedings of the 51st ACM Technical Symposium on Computer Science Education3493513UNINA04994nam 2200457 450 991056827540332120231110214919.0981-19-1797-3(MiAaPQ)EBC6965069(Au-PeEL)EBL6965069(CKB)21707968500041(PPN)262168987(EXLCZ)992170796850004120221123d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPrivacy preservation in IoT machine learning approaches : a comprehensive survey and use cases /Youyang Qu [and three others]Singapore :Springer,[2022]©20221 online resource (127 pages)SpringerBriefs in Computer Science Print version: Qu, Youyang Privacy Preservation in IoT: Machine Learning Approaches Singapore : Springer,c2022 9789811917967 Intro -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 IoT Privacy Research Landscape -- 1.2 Machine Learning Driven Privacy Preservation Overview -- 1.3 Contribution of This Book -- 1.4 Book Overview -- 2 Current Methods of Privacy Protection in IoTs -- 2.1 Briefing of Privacy Preservation Study in IoTs -- 2.2 Cryptography-Based Methods in IoTs -- 2.3 Anonymity-Based and Clustering-Based Methods -- 2.4 Differential Privacy Based Methods -- 2.5 Machine Learning and AI Methods -- 2.5.1 Federated Learning -- 2.5.2 Generative Adversarial Network -- References -- 3 Decentralized Privacy Protection of IoTs Using Blockchain-Enabled Federated Learning -- 3.1 Overview -- 3.2 Related Work -- 3.3 Architecture of Blockchain-Enabled Federated Learning -- 3.3.1 Federated Learning in FL-Block -- 3.3.2 Blockchain in FL-Block -- 3.4 Decentralized Privacy Mechanism Based on FL-Block -- 3.4.1 Blocks Establishment -- 3.4.2 Blockchain Protocols Design -- 3.4.3 Discussion on Decentralized Privacy Protection Using Blockchain -- 3.5 System Analysis -- 3.5.1 Poisoning Attacks and Defence -- 3.5.2 Single-Epoch FL-Block Latency Model -- 3.5.3 Optimal Generation Rate of Blocks -- 3.6 Performance Evaluation -- 3.6.1 Simulation Environment Description -- 3.6.2 Global Models and Corresponding Updates -- 3.6.3 Evaluation on Convergence and Efficiency -- 3.6.4 Evaluation on Blockchain -- 3.6.5 Evaluation on Poisoning Attack Resistance -- 3.7 Summary and Future Work -- References -- 4 Personalized Privacy Protection of IoTs Using GAN-Enhanced Differential Privacy -- 4.1 Overview -- 4.2 Related Work -- 4.3 Generative Adversarial Nets Driven Personalized Differential Privacy -- 4.3.1 Extended Social Networks Graph Structure -- 4.3.2 GAN with a Differential Privacy Identifier -- 4.3.3 Mapping Function.4.3.4 Opimized Trade-Off Between Personalized Privacy Protection and Optimized Data Utility -- 4.4 Attack Model and Mechanism Analysis -- 4.4.1 Collusion Attack -- 4.4.2 Attack Mechanism Analysis -- 4.5 System Analysis -- 4.6 Evaluation and Performance -- 4.6.1 Trajectory Generation Performance -- 4.6.2 Personalized Privacy Protection -- 4.6.3 Data Utility -- 4.6.4 Efficiency and Convergence -- 4.6.5 Further Discussion -- 4.7 Summary and Future Work -- References -- 5 Hybrid Privacy Protection of IoT Using Reinforcement Learning -- 5.1 Overview -- 5.2 Related Work -- 5.3 Hybrid Privacy Problem Formulation -- 5.3.1 Game-Based Markov Decision Process -- 5.3.2 Problem Formulation -- 5.4 System Modelling -- 5.4.1 Actions of the Adversary and User -- 5.4.2 System States and Transitions -- 5.4.3 Nash Equilibrium Under Game-Based MDP -- 5.5 System Analysis -- 5.5.1 Measurement of Overall Data Utility -- 5.5.2 Measurement of Privacy Loss -- 5.6 Markov Decision Process and Reinforcement Learning -- 5.6.1 Quick-Convergent Reinforcement Learning Algorithm -- 5.6.2 Best Strategy Generation with Limited Power -- 5.6.3 Best Strategy Generation with Unlimited Power -- 5.7 Performance Evaluation -- 5.7.1 Experiments Foundations -- 5.7.2 Data Utility Evaluations -- 5.7.3 Privacy Loss Evaluations -- 5.7.4 Convergence Speed -- 5.8 Summary and Future Work -- References -- 6 Future Research Directions -- 6.1 Trade-Off Optimization in IoTs -- 6.2 Privacy Preservation in Digital Twined IoTs -- 6.3 Personalized Consensus and Incentive Mechanisms for Blockchain-Enabled Federated Learning in IoTs -- 6.4 Privacy-Preserving Federated Learning in IoTs -- 6.5 Federated Generative Adversarial Network in IoTs -- 7 Summary and Outlook.SpringerBriefs in Computer Science Data privacyInternet of thingsSecurity measuresData privacy.Internet of thingsSecurity measures.323.448Qu YouyangMiAaPQMiAaPQMiAaPQBOOK9910568275403321Privacy preservation in IoT2965933UNINA