scholarly journals PCP: A Privacy-Preserving Content-Based Publish–Subscribe Scheme With Differential Privacy in Fog Computing

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 17962-17974 ◽  
Author(s):  
Qixu Wang ◽  
Dajiang Chen ◽  
Ning Zhang ◽  
Zhe Ding ◽  
Zhiguang Qin
Keyword(s):  
Differential Privacy ◽  
Fog Computing ◽  
Privacy Preserving

Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach

2019 ◽  
Vol 90 ◽  
pp. 158-174 ◽  
Author(s):  
Chunhui Piao ◽  
Yajuan Shi ◽  
Jiaqi Yan ◽  
Changyou Zhang ◽  
Liping Liu
Keyword(s):  
Differential Privacy ◽  
Fog Computing ◽  
Privacy Preserving ◽  
Data Publishing

scholarly journals Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Huo ◽  
Chengtao Yong ◽  
Yanfei Lu
Keyword(s):  
Real Time ◽  
Data Aggregation ◽  
Differential Privacy ◽  
Fog Computing ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Data Availability ◽  
Time Data ◽  
Real Time Data ◽  
Cloud Servers

In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive ω-event differential privacy (Re-ADP). Based on adaptive ω-event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

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

Privacy-preserving collaborative filtering algorithm based on local differential privacy

2021 ◽  
Vol 18 (11) ◽  
pp. 42-60
Author(s):  
Ting Bao ◽  
Lei Xu ◽  
Liehuang Zhu ◽  
Lihong Wang ◽  
Ruiguang Li ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

Privacy-Preserving Collaborative Web Services QoS Prediction via Differential Privacy

2017 ◽  
pp. 200-214 ◽  
Author(s):  
Shushu Liu ◽  
An Liu ◽  
Zhixu Li ◽  
Guanfeng Liu ◽  
Jiajie Xu ◽  
...  
Keyword(s):  
Web Services ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Qos Prediction

scholarly journals Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems

2021 ◽  
Author(s):  
Jude TCHAYE-KONDI ◽  
Yanlong Zhai ◽  
Liehuang Zhu
Keyword(s):  
Intelligent Systems ◽  
Differential Privacy ◽  
Inductive Learning ◽  
Random Noise ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Feature Extractor ◽  
Data Source ◽  
Series Of Experiments

<div>We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.</div>

Security and Privacy Challenges of Deep Learning

2020 ◽  
pp. 42-64
Author(s):  
J. Andrew Onesimu ◽  
Karthikeyan J. ◽  
D. Samuel Joshua Viswas ◽  
Robin D Sebastian
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Detailed Comparison ◽  
Privacy Preserving ◽  
Research Field ◽  
Security And Privacy ◽  
Security Attacks ◽  
Privacy Breaches ◽  
Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

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