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Auswahl der wissenschaftlichen Literatur zum Thema „Utility-privacy trade-off“
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Zeitschriftenartikel zum Thema "Utility-privacy trade-off"
Liu, Hai, Zhenqiang Wu, Yihui Zhou, Changgen Peng, Feng Tian und Laifeng Lu. „Privacy-Preserving Monotonicity of Differential Privacy Mechanisms“. Applied Sciences 8, Nr. 11 (28.10.2018): 2081. http://dx.doi.org/10.3390/app8112081.
Der volle Inhalt der QuelleAvent, Brendan, Javier González, Tom Diethe, Andrei Paleyes und Borja Balle. „Automatic Discovery of Privacy–Utility Pareto Fronts“. Proceedings on Privacy Enhancing Technologies 2020, Nr. 4 (01.10.2020): 5–23. http://dx.doi.org/10.2478/popets-2020-0060.
Der volle Inhalt der QuelleGobinathan, B., M. A. Mukunthan, S. Surendran, K. Somasundaram, Syed Abdul Moeed, P. Niranjan, V. Gouthami et al. „A Novel Method to Solve Real Time Security Issues in Software Industry Using Advanced Cryptographic Techniques“. Scientific Programming 2021 (28.12.2021): 1–9. http://dx.doi.org/10.1155/2021/3611182.
Der volle Inhalt der QuelleZeng, Xia, Chuanchuan Yang und Bin Dai. „Utility–Privacy Trade-Off in Distributed Machine Learning Systems“. Entropy 24, Nr. 9 (14.09.2022): 1299. http://dx.doi.org/10.3390/e24091299.
Der volle Inhalt der QuelleSrivastava, Saurabh, Vinay P. Namboodiri und T. V. Prabhakar. „Achieving Privacy-Utility Trade-off in existing Software Systems“. Journal of Physics: Conference Series 1454 (Februar 2020): 012004. http://dx.doi.org/10.1088/1742-6596/1454/1/012004.
Der volle Inhalt der QuelleWunderlich, Dominik, Daniel Bernau, Francesco Aldà, Javier Parra-Arnau und Thorsten Strufe. „On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text Classification“. Applied Sciences 12, Nr. 21 (04.11.2022): 11177. http://dx.doi.org/10.3390/app122111177.
Der volle Inhalt der QuelleMohammed, Kabiru, Aladdin Ayesh und Eerke Boiten. „Complementing Privacy and Utility Trade-Off with Self-Organising Maps“. Cryptography 5, Nr. 3 (17.08.2021): 20. http://dx.doi.org/10.3390/cryptography5030020.
Der volle Inhalt der QuelleKiranagi, Manasi, Devika Dhoble, Madeeha Tahoor und Dr Rekha Patil. „Finding Optimal Path and Privacy Preserving for Wireless Network“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 10 (31.10.2022): 360–65. http://dx.doi.org/10.22214/ijraset.2022.46949.
Der volle Inhalt der QuelleCai, Lin, Jinchuan Tang, Shuping Dang und Gaojie Chen. „Privacy protection and utility trade-off for social graph embedding“. Information Sciences 676 (August 2024): 120866. http://dx.doi.org/10.1016/j.ins.2024.120866.
Der volle Inhalt der QuelleRassouli, Borzoo, und Deniz Gunduz. „Optimal Utility-Privacy Trade-Off With Total Variation Distance as a Privacy Measure“. IEEE Transactions on Information Forensics and Security 15 (2020): 594–603. http://dx.doi.org/10.1109/tifs.2019.2903658.
Der volle Inhalt der QuelleDissertationen zum Thema "Utility-privacy trade-off"
Aldà, Francesco [Verfasser], Hans Ulrich [Gutachter] Simon und Alexander [Gutachter] May. „On the trade-off between privacy and utility in statistical data analysis / Francesco Aldà ; Gutachter: Hans Ulrich Simon, Alexander May ; Fakultät für Mathematik“. Bochum : Ruhr-Universität Bochum, 2018. http://d-nb.info/1161942416/34.
Der volle Inhalt der QuelleKaplan, Caelin. „Compromis inhérents à l'apprentissage automatique préservant la confidentialité“. Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4045.
Der volle Inhalt der QuelleAs machine learning (ML) models are increasingly integrated into a wide range of applications, ensuring the privacy of individuals' data is becoming more important than ever. However, privacy-preserving ML techniques often result in reduced task-specific utility and may negatively impact other essential factors like fairness, robustness, and interpretability. These challenges have limited the widespread adoption of privacy-preserving methods. This thesis aims to address these challenges through two primary goals: (1) to deepen the understanding of key trade-offs in three privacy-preserving ML techniques—differential privacy, empirical privacy defenses, and federated learning; (2) to propose novel methods and algorithms that improve utility and effectiveness while maintaining privacy protections. The first study in this thesis investigates how differential privacy impacts fairness across groups defined by sensitive attributes. While previous assumptions suggested that differential privacy could exacerbate unfairness in ML models, our experiments demonstrate that selecting an optimal model architecture and tuning hyperparameters for DP-SGD (Differentially Private Stochastic Gradient Descent) can mitigate fairness disparities. Using standard ML fairness datasets, we show that group disparities in metrics like demographic parity, equalized odds, and predictive parity are often reduced or remain negligible when compared to non-private baselines, challenging the prevailing notion that differential privacy worsens fairness for underrepresented groups. The second study focuses on empirical privacy defenses, which aim to protect training data privacy while minimizing utility loss. Most existing defenses assume access to reference data---an additional dataset from the same or a similar distribution as the training data. However, previous works have largely neglected to evaluate the privacy risks associated with reference data. To address this, we conducted the first comprehensive analysis of reference data privacy in empirical defenses. We proposed a baseline defense method, Weighted Empirical Risk Minimization (WERM), which allows for a clearer understanding of the trade-offs between model utility, training data privacy, and reference data privacy. In addition to offering theoretical guarantees on model utility and the relative privacy of training and reference data, WERM consistently outperforms state-of-the-art empirical privacy defenses in nearly all relative privacy regimes.The third study addresses the convergence-related trade-offs in Collaborative Inference Systems (CISs), which are increasingly used in the Internet of Things (IoT) to enable smaller nodes in a network to offload part of their inference tasks to more powerful nodes. While Federated Learning (FL) is often used to jointly train models within CISs, traditional methods have overlooked the operational dynamics of these systems, such as heterogeneity in serving rates across nodes. We propose a novel FL approach explicitly designed for CISs, which accounts for varying serving rates and uneven data availability. Our framework provides theoretical guarantees and consistently outperforms state-of-the-art algorithms, particularly in scenarios where end devices handle high inference request rates.In conclusion, this thesis advances the field of privacy-preserving ML by addressing key trade-offs in differential privacy, empirical privacy defenses, and federated learning. The proposed methods provide new insights into balancing privacy with utility and other critical factors, offering practical solutions for integrating privacy-preserving techniques into real-world applications. These contributions aim to support the responsible and ethical deployment of AI technologies that prioritize data privacy and protection
Buchteile zum Thema "Utility-privacy trade-off"
Alvim, Mário S., Miguel E. Andrés, Konstantinos Chatzikokolakis, Pierpaolo Degano und Catuscia Palamidessi. „Differential Privacy: On the Trade-Off between Utility and Information Leakage“. In Lecture Notes in Computer Science, 39–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29420-4_3.
Der volle Inhalt der QuelleTang, Jingye, Tianqing Zhu, Ping Xiong, Yu Wang und Wei Ren. „Privacy and Utility Trade-Off for Textual Analysis via Calibrated Multivariate Perturbations“. In Network and System Security, 342–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65745-1_20.
Der volle Inhalt der QuelleRafiei, Majid, Frederik Wangelik und Wil M. P. van der Aalst. „TraVaS: Differentially Private Trace Variant Selection for Process Mining“. In Lecture Notes in Business Information Processing, 114–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_9.
Der volle Inhalt der QuelleThouvenot, Maxime, Olivier Curé, Lynda Temal, Sarra Ben Abbès und Philippe Calvez. „Knowledge Graph Publishing with Anatomy, Toward a New Privacy and Utility Trade-Off“. In Advances in Knowledge Discovery and Management, 55–79. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-40403-0_3.
Der volle Inhalt der QuellePeng, Lian, und Meikang Qiu. „AI in Healthcare Data Privacy-Preserving: Enhanced Trade-Off Between Security and Utility“. In Knowledge Science, Engineering and Management, 349–60. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5498-4_27.
Der volle Inhalt der QuelleLiu, Yang, und Andrew Simpson. „On the Trade-Off Between Privacy and Utility in Mobile Services: A Qualitative Study“. In Computer Security, 261–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42048-2_17.
Der volle Inhalt der QuelleGhatak, Debolina, und Kouichi Sakurai. „A Survey on Privacy Preserving Synthetic Data Generation and a Discussion on a Privacy-Utility Trade-off Problem“. In Communications in Computer and Information Science, 167–80. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7769-5_13.
Der volle Inhalt der QuelleDemir, Mehmet Özgün, Ali Emre Pusane, Guido Dartmann und Güneş Karabulut Kurt. „Utility privacy trade-off in communication systems“. In Big Data Analytics for Cyber-Physical Systems, 293–314. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-816637-6.00014-2.
Der volle Inhalt der QuelleKaabachi, Bayrem, Jérémie Despraz, Thierry Meurers, Fabian Prasser und Jean Louis Raisaro. „Generation and Evaluation of Synthetic Data in a University Hospital Setting“. In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220420.
Der volle Inhalt der QuelleCorbucci, Luca, Mikko A. Heikkilä, David Solans Noguero, Anna Monreale und Nicolas Kourtellis. „PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240671.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Utility-privacy trade-off"
Pohlhausen, Jule, Francesco Nespoli und Joerg Bitzer. „Long-Term Conversation Analysis: Privacy-Utility Trade-Off Under Noise and Reverberation“. In 2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC), 404–8. IEEE, 2024. http://dx.doi.org/10.1109/iwaenc61483.2024.10694640.
Der volle Inhalt der QuelleNam, Seung-Hyun, Hyun-Young Park und Si-Hyeon Lee. „Achieving the Exactly Optimal Privacy-Utility Trade-Off with Low Communication Cost via Shared Randomness“. In 2024 IEEE International Symposium on Information Theory (ISIT), 3065–70. IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619385.
Der volle Inhalt der QuelleBaselizadeh, Adel, Diana Saplacan Lindblom, Weria Khaksar, Md Zia Uddin und Jim Torresen. „Comparative Analysis of Vision-Based Sensors for Human Monitoring in Care Robots: Exploring the Utility-Privacy Trade-off“. In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), 1794–801. IEEE, 2024. http://dx.doi.org/10.1109/ro-man60168.2024.10731223.
Der volle Inhalt der QuelleErdogdu, Murat A., und Nadia Fawaz. „Privacy-utility trade-off under continual observation“. In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282766.
Der volle Inhalt der QuelleZhou, Yihui, Guangchen Song, Hai Liu und Laifeng Lu. „Privacy-Utility Trade-Off of K-Subset Mechanism“. In 2018 International Conference on Networking and Network Applications (NaNA). IEEE, 2018. http://dx.doi.org/10.1109/nana.2018.8648741.
Der volle Inhalt der QuelleLi, Mengqian, Youliang Tian, Junpeng Zhang, Dandan Fan und Dongmei Zhao. „The Trade-off Between Privacy and Utility in Local Differential Privacy“. In 2021 International Conference on Networking and Network Applications (NaNA). IEEE, 2021. http://dx.doi.org/10.1109/nana53684.2021.00071.
Der volle Inhalt der QuelleSreekumar, Sreejith, und Deniz Gunduz. „Optimal Privacy-Utility Trade-off under a Rate Constraint“. In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849330.
Der volle Inhalt der QuelleAlvim, Mário S., Natasha Fernandes, Annabelle McIver und Gabriel H. Nunes. „The Privacy-Utility Trade-off in the Topics API“. In CCS '24: ACM SIGSAC Conference on Computer and Communications Security, 1106–20. New York, NY, USA: ACM, 2024. https://doi.org/10.1145/3658644.3670368.
Der volle Inhalt der QuelleZhou, Jinhao, Zhou Su, Jianbing Ni, Yuntao Wang, Yanghe Pan und Rui Xing. „Personalized Privacy-Preserving Federated Learning: Optimized Trade-off Between Utility and Privacy“. In GLOBECOM 2022 - 2022 IEEE Global Communications Conference. IEEE, 2022. http://dx.doi.org/10.1109/globecom48099.2022.10000793.
Der volle Inhalt der QuelleDemir, Mehmet Oezguen, Selahattin Goekceli, Guido Dartmann, Volker Luecken, Gerd Ascheid und Guenes Karabulut Kurt. „Utility Privacy Trade-Off for Noisy Channels in OFDM Systems“. In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). IEEE, 2017. http://dx.doi.org/10.1109/vtcfall.2017.8288194.
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