($$k,\varepsilon ,\delta $$)-Anonymization: privacy-preserving data release based on k-anonymity and differential privacy

2021 ◽  
Author(s):  
Yao-Tung Tsou ◽  
Mansour Naser Alraja ◽  
Li-Sheng Chen ◽  
Yu-Hsiang Chang ◽  
Yung-Li Hu ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving ◽  
Data Release

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

EPPD: Efficient and privacy-preserving proximity testing with differential privacy techniques

2016 ◽  
Author(s):  
Cheng Huang ◽  
Rongxing Lu ◽  
Hui Zhu ◽  
Jun Shao ◽  
Abdulrahman Alamer ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving

scholarly journals RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release

2019 ◽  
Vol 2019 (1) ◽  
pp. 26-46 ◽  
Author(s):  
Thee Chanyaswad ◽  
Changchang Liu ◽  
Prateek Mittal
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Differential Privacy ◽  
Real Data ◽  
The Novel ◽  
Private Data ◽  
Data Release ◽  
Machine Learning Applications ◽  
Order Of Magnitude ◽  
Real World Datasets

Abstract A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present multiple algorithms for a range of machine learning applications, including both unsupervised and supervised learning. Furthermore, we rigorously prove that (a) our algorithms satisfy the strong ɛ-differential privacy guarantee, and (b) RON projection can lower the level of perturbation required for differential privacy. Finally, we illustrate the effectiveness of RON-Gauss under three common machine learning applications – clustering, classification, and regression – on three large real-world datasets. Our empirical results show that (a) RON-Gauss outperforms previous approaches by up to an order of magnitude, and (b) loss in utility compared to the non-private real data is small. Thus, RON-Gauss can serve as a key enabler for real-world deployment of privacy-preserving data release.

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>

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
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