Searched for: subject%3A%22differential%255C+privacy%22
(1 - 16 of 16)
document
Van Opstal, Quinten (author)
Federated learning provides a lot of opportunities, especially with the built-in privacy considerations. There is however one attack that might compromise the utility of federated learning: backdoor attacks [14]. There are already some existing defenses, like flame [13] but they are computationally expensive [14]. This paper evaluates a version...
bachelor thesis 2024
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Li, Qiongxiu (author), Gundersen, Jaron Skovsted (author), Lopuhaa-Zwakenberg, Milan (author), Heusdens, R. (author)
Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches...
journal article 2024
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Li, T. (author), Xu, L. (author), Erkin, Z. (author), Lagendijk, R.L. (author)
With the fast development of e-commerce, there is a higher demand for timely delivery. Logistic companies want to send receivers a more accurate arrival prediction to improve customer satisfaction and lower customer retention costs. One approach is to share (near) real-time location data with recipients, but this also introduces privacy and...
conference paper 2024
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Ghavamipour, Ali Reza (author), Turkmen, Fatih (author), Wang, Rui (author), Liang, K. (author)
Synthetic data generation plays a crucial role in many areas where data is scarce and privacy/confidentiality is a significant concern. Generative Adversarial Networks (GANs), arguably one of the most widely used data synthesis techniques, allow for the training of a model (i.e., generator) that can generate real-looking data by playing a min...
conference paper 2023
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de With, Wim (author)
Recommender systems usually base their predictions on user-item interaction, a technique known as collaborative filtering. Vendors that utilize collaborative filtering generally exclusively use their own user-item interactions, but the accuracy of the recommendations may improve if several vendors share their data. Since user-item interaction...
master thesis 2022
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Tian, Yuhang (author)
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring...
master thesis 2022
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Karahan, Asli (author)
Healthcare recommender systems emerged to help patients make better decisions for their health, leveraging the vast amount of data and patient experience. One type of this system focuses on recommending the most appropriate physician based on previous patient feedback in the form of ratings. Such advice can be challenging to generate for new...
master thesis 2022
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Schram, Gregor (author)
Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work with ever more privacy-sensitive data. In order to maintain user privacy, a combination of federated learning,...
bachelor thesis 2022
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Li, T. (author), Erkin, Z. (author), Lagendijk, R.L. (author)
With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service. Information leakage during transportation is an important reason for theft since thieves can identify which truck is...
journal article 2022
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Kunar, Aditya (author)
While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art...
master thesis 2021
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te Marvelde, Pepijn (author)
Generative Adversarial Networks (GANs) are a modern solution aiming to encourage public sharing of data, even if the data contains inherently private information, by generating synthetic data that looks like, but is not equal to, the data the GAN was trained on. However, GANs are prone to remembering samples from the training data, therefore...
bachelor thesis 2021
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Filip, Eduard (author)
Federated Learning starts to give a new perspective regarding the applicability of machine learning in real-life scenarios. Its main goal is to train the model while keeping the participants' data in their devices, thus guaranteeing the privacy of their data. One of the main architectures is the Horizontal Federated Learning, which is the most...
bachelor thesis 2021
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Wang, Jin (author), Han, Hui (author), Li, H. (author), He, Shiming (author), Sharma, Pradip Kumar (author), Chen, Lydia Y. (author)
Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension reduction to analyze high-order, high-dimension, and sparse tensor (HOHDST) data, which is transmitted on 5G Internet-of-things (IoT). Hence, HOHDST data relies...
journal article 2021
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Li, Qiongxiu (author), Gundersen, Jaron Skovsted (author), Heusdens, R. (author), Christensen, Mads Græsbøll (author)
Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many existing algorithms can be adopted to solve this problem such as differential privacy, secure multiparty...
journal article 2021
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Nandakumar, Lakshminarayanan (author), Ferrari, Riccardo M.G. (author), Keviczky, T. (author)
Releasing state samples generated by a dynamical system model, for data aggregation purposes, can allow an adversary to perform reverse engineering and estimate sensitive model parameters. Upon identification of the system model, the adversary may even use it for predicting sensitive data in the future. Hence, preserving a confidential dynamical...
journal article 2019
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Rostampour, Vahab (author), Ferrari, Riccardo M.G. (author), Teixeira, André M.H. (author), Keviczky, T. (author)
Distributed fault diagnosis has been proposed as an effective technique for monitoring large scale, nonlinear and uncertain systems. It is based on the decomposition of the large scale system into a number of interconnected subsystems, each one monitored by a dedicated Local Fault Detector (LFD). Neighboring LFDs, in order to successfully...
journal article 2018
Searched for: subject%3A%22differential%255C+privacy%22
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