方班隐私保护小组

2023年2月24日

【五期邹昱夫】CCF-A（CCS）Membership Inference Attacks by Exploiting Loss Trajectory

摘要： "Liu, Yiyong, et al. "Membership inference attacks by exploiting loss trajectory." Proceedings of the 2022 ACM SIGSAC Conference on Computer and Commu 阅读全文

posted @ 2023-02-24 21:12 方班隐私保护小组阅读(179) 评论(2) 推荐(0) 编辑

【五期杨志】CCF-A（CVPR'20） End-to-End Object Detection with Transformers

摘要： Carion, Nicolas, et al. "End-to-end object detection with transformers." European conference on computer vision. Springer, Cham, 2020. 针对传统目标检测算法中存在的一阅读全文

posted @ 2023-02-24 13:23 方班隐私保护小组阅读(34) 评论(0) 推荐(0) 编辑

2023年2月23日

【五期邹昱夫】CCF-A（ICCV'21）On the Difficulty of Membership Inference Attacks

摘要： "Rezaei, Shahbaz, and Xin Liu. "On the difficulty of membership inference attacks." Proceedings of the IEEE/CVF Conference on Computer Vision and Patt 阅读全文

posted @ 2023-02-23 21:58 方班隐私保护小组阅读(63) 评论(0) 推荐(0) 编辑

【五期李伟平】CCF-C（ICC'19）Efficient and Secure k-Nearest Neighbor Search Over Encrypted Data in Public Cloud

摘要： Song, F. , et al. "Efficient and Secure k-Nearest Neighbor Search Over Encrypted Data in Public Cloud." ICC 2019 - 2019 IEEE International Conference 阅读全文

posted @ 2023-02-23 10:01 方班隐私保护小组阅读(38) 评论(0) 推荐(0) 编辑

2023年2月22日

【五期李伟平】CCF-C（Applied Intelligence'16）Outlier-eliminated k-means clustering algorithm based on differential privacy preservation

摘要： Luo, et al. "Outlier-eliminated k-means clustering algorithm based on differential privacy preservation." Applied Intelligence the International Journ 阅读全文

posted @ 2023-02-22 22:15 方班隐私保护小组阅读(49) 评论(0) 推荐(0) 编辑

2023年2月10日

【五期邹昱夫】CCF-A（IEEE'19）Adversary instantiation: Lower bounds for differentially private machine learning

摘要： "Nasr M, Songi S, Thakurta A, et al. Adversary instantiation: Lower bounds for differentially private machine learning[C]//2021 IEEE Symposium on secu 阅读全文

posted @ 2023-02-10 23:59 方班隐私保护小组阅读(28) 评论(0) 推荐(0) 编辑

【五期李伟平】CCF-B（CIKM'20）PrivacyFL：A Simulator for Privacy-Preserving and Secure Federated Learning

摘要： Mugunthan, V. , A. Peraire-Bueno , and L. Kagal . "PrivacyFL: A simulator for privacy-preserving and secure federated learning.", 10.1145/3340531.3412 阅读全文

posted @ 2023-02-10 23:36 方班隐私保护小组阅读(42) 评论(0) 推荐(0) 编辑

【五期李伟平】CCF-A（AAAI'20）SplitFed: When Federated Learning Meets Split Learning

摘要： Thapa, C. , M. Chamikara , and S. Camtepe . "SplitFed: When Federated Learning Meets Split Learning." (2020). 本文提出了一种联邦学习（FL）和分割学习（SL）的混合方法（SFL），能够同时解阅读全文

posted @ 2023-02-10 23:35 方班隐私保护小组阅读(209) 评论(0) 推荐(0) 编辑

【五期邹昱夫】CCF-A（USENIX'19）Evaluating Differentially Private Machine Learning in Practice

摘要： "Jayaraman B, Evans D. Evaluating differentially private machine learning in practice[C]//USENIX Security Symposium. 2019." 本文对机器学习不同隐私机制进行评估。评估重点放在梯度阅读全文

posted @ 2023-02-10 23:09 方班隐私保护小组阅读(29) 评论(0) 推荐(0) 编辑

2023年2月3日

【五期邹昱夫】CCF-A（IEEE'22）Dynamic Backdoor Attacks Against Machine Learning Models

摘要： "Salem A, Wen R, Backes M, et al. Dynamic backdoor attacks against machine learning models[C]//2022 IEEE 7th European Symposium on Security and Privac 阅读全文

posted @ 2023-02-03 16:54 方班隐私保护小组阅读(84) 评论(0) 推荐(0) 编辑

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