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of 102
pro vyhledávání: '"Gursoy, Mehmet"'
Local differential privacy (LDP) has recently emerged as a popular privacy standard. With the growing popularity of LDP, several recent works have applied LDP to spatial data, and grid-based decompositions have been a common building block in the col
Externí odkaz:
http://arxiv.org/abs/2407.21624
Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in
Externí odkaz:
http://arxiv.org/abs/2009.06505
Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only model updates being shared with a central server. However, the distributed
Externí odkaz:
http://arxiv.org/abs/2007.08432
Deep neural networks based object detection models have revolutionized computer vision and fueled the development of a wide range of visual recognition applications. However, recent studies have revealed that deep object detectors can be compromised
Externí odkaz:
http://arxiv.org/abs/2007.05828
This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or cate
Externí odkaz:
http://arxiv.org/abs/2006.03637
Autor:
Wei, Wenqi, Liu, Ling, Loper, Margaret, Chow, Ka-Ho, Gursoy, Mehmet Emre, Truex, Stacey, Wu, Yanzhao
Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client privacy by allowing clients to keep their sensitive data on local device
Externí odkaz:
http://arxiv.org/abs/2004.10397
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems. Real-time object recognition on the edge is one of the representative deep neural network (DNN) powered edge systems for r
Externí odkaz:
http://arxiv.org/abs/2004.04320
Akademický článek
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Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three unique contributions. First, t
Externí odkaz:
http://arxiv.org/abs/1911.09777