scholarly journals Achieving Privacy-Preserving Group Recommendation with Local Differential Privacy and Random Transmission

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hanyi Wang ◽  
Kun He ◽  
Ben Niu ◽  
Lihua Yin ◽  
Fenghua Li
Keyword(s):  
Differential Privacy ◽  
Historical Data ◽  
Privacy Preserving ◽  
Personalized Recommendation ◽  
Preference Aggregation ◽  
Group Recommendation ◽  
Recommendation Algorithm ◽  
Privacy Leakage ◽  
Long Term Observation

Group activities on social networks are increasing rapidly with the development of mobile devices and IoT terminals, creating a huge demand for group recommendation. However, group recommender systems are facing an important problem of privacy leakage on user’s historical data and preference. Existing solutions always pay attention to protect the historical data but ignore the privacy of preference. In this paper, we design a privacy-preserving group recommendation scheme, consisting of a personalized recommendation algorithm and a preference aggregation algorithm. With the carefully introduced local differential privacy (LDP), our personalized recommendation algorithm can protect user’s historical data in each specific group. We also propose an Intra-group transfer Privacy-preserving Preference Aggregation algorithm (IntPPA). IntPPA protects each group member’s personal preference against either the untrusted servers or other users. It could also defend long-term observation attack. We also conduct several experiments to measure the privacy-preserving effect and usability of our scheme with some closely related schemes. Experimental results on two datasets show the utility and privacy of our scheme and further illustrate its advantages.

Privacy-Preserving Mechanisms for Multi-Label Image Recognition

2022 ◽  
Vol 16 (4) ◽  
pp. 1-21
Author(s):  
Honghui Xu ◽  
Zhipeng Cai ◽  
Wei Li
Keyword(s):  
Image Recognition ◽  
Loss Function ◽  
Prediction Accuracy ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Global Sensitivity ◽  
Regularization Term ◽  
Convolutional Networks ◽  
Privacy Leakage ◽  
Computer Vision Applications

Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.

scholarly journals SAT0137 Long-term observation of fibromyalgia patients

2001 ◽  
Author(s):  
L Szczepanski ◽  
C Stepniak ◽  
B Targonska-Stepniak
Keyword(s):  
Long Term Observation

Scalable Privacy-preserving Geo-distance Evaluation for Precision Agriculture IoT Systems

2021 ◽  
Vol 17 (4) ◽  
pp. 1-30
Author(s):  
Qiben Yan ◽  
Jianzhi Lou ◽  
Mehmet C. Vuran ◽  
Suat Irmak
Keyword(s):  
Precision Agriculture ◽  
Evaluation System ◽  
Privacy Preserving ◽  
Large Network ◽  
Additional Information ◽  
Modern Agriculture ◽  
Iot Applications ◽  
Privacy Leakage ◽  
Practical Performance ◽  
Real World Datasets

Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, and reducing cost. However, the mass collection of farm data in IoT applications raises serious concerns about potential privacy leakage that may harm the farmers’ welfare. In this work, we propose a novel scalable and private geo-distance evaluation system, called SPRIDE, to allow application servers to provide geographic-based services by computing the distances among sensors and farms privately. The servers determine the distances without learning any additional information about their locations. The key idea of SPRIDE is to perform efficient distance measurement and distance comparison on encrypted locations over a sphere by leveraging a homomorphic cryptosystem. To serve a large user base, we further propose SPRIDE+ with novel and practical performance enhancements based on pre-computation of cryptographic elements. Through extensive experiments using real-world datasets, we show SPRIDE+ achieves private distance evaluation on a large network of farms, attaining 3+ times runtime performance improvement over existing techniques. We further show SPRIDE+ can run on resource-constrained mobile devices, which offers a practical solution for privacy-preserving precision agriculture IoT applications.

Privacy-preserving Streaming Truth Discovery in Crowdsourcing with Differential Privacy

2021 ◽  
pp. 1-1
Author(s):  
Dan Wang ◽  
Ju Ren ◽  
Zhibo Wang ◽  
Xiaoyi Pang ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving ◽  
Truth Discovery
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