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Detecting Communities under Differential Privacy [article]

Hiep H. Nguyen, Abdessamad Imine, Michael Rusinowitch
2016 arXiv   pre-print
In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data.  ...  Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks.  ...  Challenges of Community Detection under Differential Privacy In this section, we explain why community detection under differential privacy is challenging.  ... 
arXiv:1607.02060v1 fatcat:nsvxlgvbj5ffhfr5fqqpuz5q6e

Detecting Communities under Differential Privacy

Hiep H. Nguyen, Abdessamad Imine, Michaël Rusinowitch
2016 Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society - WPES'16  
In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data.  ...  Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks.  ...  Challenges of Community Detection under Differential Privacy In this section, we explain why community detection under differential privacy is challenging.  ... 
doi:10.1145/2994620.2994624 fatcat:pl6ufsenpzf43psxcp24pgvb4e

Detecting Anomalous LAN Activities under Differential Privacy

Norrathep Rattanavipanon, Donlapark Ponnoprat, Hideya Ochiai, Kuljaree Tantayakul, Touchai Angchuan, Sinchai Kamolphiwong, George Drosatos
2022 Security and Communication Networks  
We present four approaches, namely, naïve, histogram-based, naïve- δ , and histogram-based- δ and show that they satisfy different levels of differential privacy—a rigorous and provable notion for quantifying  ...  With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data.  ...  property of the differential privacy is that it is preserved under post-processing.  ... 
doi:10.1155/2022/1403200 fatcat:7tv6j4qz7nh6pfr4p5qykueucy

Differentially-Private Distributed Fault Diagnosis for Large-Scale Nonlinear Uncertain Systems

Vahab Rostampour, Riccardo Ferrari, André M.H. Teixeira, Tamás Keviczky
2018 IFAC-PapersOnLine  
To avoid this problem, we propose here to use differential privacy to pre-process data before transmission.  ...  To avoid this problem, we propose here to use differential privacy to pre-process data before transmission.  ...  Then the mechanism M preserves −differential privacy. Privacy-Preserving Mechanism The proposed privacy-preserving framework for distributed fault detection will be now presented.  ... 
doi:10.1016/j.ifacol.2018.09.703 fatcat:py7ojmssxra5hiqnpwxahpkmei

LDPCD: A Novel Method for Locally Differentially Private Community Detection

Zhejian Zhang, Bai Yuan Ding
2022 Computational Intelligence and Neuroscience  
As a result, community detection in social graphs under local differential privacy has gradually aroused the interest of industry and academia.  ...  For these reasons, a new community detection method based on the local differential privacy model (named LDPCD) is proposed in this paper.  ...  In summary, the following contributions of this paper are made: (1 )A novel community detection method LDPCD under local differential privacy protection is proposed, which can obtain better community detection  ... 
doi:10.1155/2022/4080047 pmid:35047034 pmcid:PMC8763540 fatcat:5sp2aw4hevaj5hnfts2hvefppe

Differentially Private Release of the Distribution of Clustering Coefficients across Communities

Xiaoye Li, Jing Yang, Zhenlong Sun, Jianpei Zhang
2019 Security and Communication Networks  
The experimental results indicate that the proposed method provides valuable distribution results while guaranteeing ε-differential privacy.  ...  clustering coefficients across communities.  ...  [8] addressed the problem of detecting communities under differential privacy. They proposed two schemes, input perturbation and algorithm perturbation.  ... 
doi:10.1155/2019/2518714 fatcat:g64zxvtikrdsnbbtvcqjell4zu

DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

Songyan Li, Zhaobin Liu, Zhiyi Huang, Haoze Lyu, Zhiyang Li, Weijiang Liu
2019 IEEE Access  
INDEX TERMS Differential privacy, WSN, IoT, dynamic networks, sensor layer. This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Theoretical analysis and experimental results show that DynaPro can preserve the important network features of the original network topology under the premise of the differential privacy protection model  ...  Network community detection can be divided into dynamic community detection and static community detection.  ... 
doi:10.1109/access.2019.2953470 fatcat:tuu37mffnzdnbdunnppi4edpmq

Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

Jibo Shi, Lin Qi, Kuixian Li, Yun Lin, Jinbo Xiong
2021 Wireless Communications and Mobile Computing  
In this article, we will examine the recognition of signal modulation based on federated learning with differential privacy, and the results show that the recognition rate is acceptable while data protection  ...  Signal modulation recognition is widely utilized in the field of spectrum detection, channel estimation, and interference recognition.  ...  We added discussion and experimental verification on differential privacy content.  ... 
doi:10.1155/2021/2537546 fatcat:whaqijhumzfpdcmoi62fjbobge

Quantifying the Tradeoff Between Cybersecurity and Location Privacy [article]

Dajiang Suo, M. Elena Renda, Jinhua Zhao
2021 arXiv   pre-print
While LBS providers could adopt privacy preservation mechanisms to obfuscate customer data, the accuracy of vehicle location data and trajectories is crucial for detecting anomalies, especially when machine  ...  The experimental results suggest that, by applying privacy on location data, DBSCAN is more sensitive to Laplace noise than RNN, although they achieve similar detection accuracy on the trip data without  ...  between DBSCAN and RNN under different differential privacy levels.  ... 
arXiv:2105.01262v2 fatcat:yick5s644vfo5pq4rr7svrxcdi

Counterterrorism: Privately Clustering a Radical Social Network Data

Jamal Boujmil, N. Tagmouti, N. Raissouni
2017 Transactions on Machine Learning and Artificial Intelligence  
Differential privacy provides one of the strongest privacy guarantees up to now.  ...  The tradeoff between the needed or essential gathering and analysis of personal data and the privacy rights of individuals is now an important requirement under any counterterrorism program.  ...  No one of them use it for a differential privacy community detection purpose.  ... 
doi:10.14738/tmlai.54.3204 fatcat:qamicvt2wzgxxbey2bkwl7fn5q

Towards Privacy-Preserving Driver's Drowsiness and Distraction Detection: A Differential Privacy Approach

Mahmoud Raafat,et al.
2016 International Journal of Computing and Digital Systems  
In this paper, we present a novel technique called block Laplacian Obfuscation Mechanism (bLOM), to privatize the camera data stream by using differential privacy techniques introduced in database domain  ...  We introduce a metric to measure privacy and utility for driver's drowsiness and distraction algorithms.  ...  Preliminaries While the notion of differential privacy was originally defined for databases, the definitions do not cover directly computer vision and image processing domain under consideration.  ... 
doi:10.12785/ijcds/050501 fatcat:d7uvgfuocjh6lpfx3dozfzapsm

Differentially Private Recommendation System Based on Community Detection in Social Network Applications

Gesu Li, Zhipeng Cai, Guisheng Yin, Zaobo He, Madhuri Siddula
2018 Security and Communication Networks  
Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.  ...  This method provides a list of recommendations for target attributes based on community detection and known user attributes and links.  ...  The first part is a differential privacy based on community detection. The second part is the differential privacy based on Naive Bayes [52] . Differential Privacy Based on Community Detection.  ... 
doi:10.1155/2018/3530123 fatcat:f52k6gn2grdgbkqtkqerwfjl5m

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

Changqiao Xu, Liang Zhu, Yang Liu, Jianfen Guan, Shui Yu
2018 IEEE Transactions on Services Computing  
In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory  ...  Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks.  ...  As DP-LTOD is the first method that supports the latent trajectory community discovering under differential privacy, we compare the algorithm DP-LTOD with two differential privacy trajectory publishing  ... 
doi:10.1109/tsc.2018.2855740 fatcat:rzhpagpn4ngnhfgc44qsaywcbm

A Survey on Security and Privacy in Emerging Sensor Networks: From Viewpoint of Close-Loop

Lifu Zhang, Heng Zhang
2016 Sensors  
This paper also discusses several future research directions under these two umbrellas.  ...  Cyber-Physical Systems (CPSs) refer to the complex networked systems that have both physical subsystems and cyber components, and the information flow between different subsystems and components is across a communication  ...  Differential privacy is adopted to guarantee the customers' privacy requirements. Huang et al.  ... 
doi:10.3390/s16040443 pmid:27023559 pmcid:PMC4850957 fatcat:ndpgvn3rdjhtvldrl2ih7lsvuu

Protecting vehicular networks privacy in the presence of a single adversarial authority

Chang-Wu Chen, Sang-Yoon Chang, Yih-Chun Hu, Yen-Wen Chen
2017 2017 IEEE Conference on Communications and Network Security (CNS)  
We also analyze our solution using techniques from differential privacy and validate it using traffic-simulator based experiments.  ...  We propose a solution that accommodates threshold-based detection, but uses relabeling and noise to limit the information that can be learned from a single insider adversary.  ...  Our revised scheme is privacy-preserving under a single malicious authority, and uses re-labeling and differential privacy to protect privacy.  ... 
doi:10.1109/cns.2017.8228648 dblp:conf/cns/ChenCHC17 fatcat:n5simiuwvvcvdheebhyhfmqiby
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