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Histogram Publication over Numerical Values under Local Differential Privacy

Xu Zheng, Ke Yan, Jingyuan Duan, Wenyi Tang, Ling Tian, Yingjie Wang
2021 Wireless Communications and Mobile Computing  
Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection.  ...  To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios.  ...  One data curator collects the contents from contributors and publishes them to data consumers.  ... 
doi:10.1155/2021/8886255 fatcat:fdgkm5ehtvfg5izhhsmmgxjgra

Privacy-Preserving Data Aggregation Framework for Mobile Service Based Multiuser Collaboration

Hai Liu, Zhenqiang Wu, Changgen Peng, Feng Tian, Laifeng Lu
2019 ˜The œinternational Arab journal of information technology  
Considering the untrusted server, differential privacy and local differential privacy has been used for privacy-preserving in data aggregation.  ...  Through our analysis, differential privacy and local differential privacy cannot achieve Nash equilibrium between privacy and utility for mobile service based multiuser collaboration, which is multiuser  ...  .  We analyzed differential privacy and local differential privacy no satisfying Nash equilibrium between privacy and utility for mobile service based multiuser collaboration in a data aggregation setting  ... 
doi:10.34028/iajit/17/4/3 fatcat:sdwl7gy2fvetxgogy7szmaet3y

Privacy in Internet of Things: from Principles to Technologies

Chao Li, Balaji Palanisamy
2018 IEEE Internet of Things Journal  
Many services in the IoT may require a comprehensive understanding and analysis of data collected through a large number of physical devices that challenges both personal information privacy and the development  ...  Information privacy in IoT is a broad and complex concept as its understanding and perception differ among individuals and its enforcement requires efforts from both legislation as well as technologies  ...  in IoT by analyzing their performance under the following IoT specific challenges [5] : • Large data volume: The gateways may control thousands of sensors that collect massive data. • Streaming data  ... 
doi:10.1109/jiot.2018.2864168 fatcat:ylasv2g72bghfnzu3do4wiksxq

A Sampling-Based Method for Highly Efficient Privacy-Preserving Data Publication

Guoming Lu, Xu Zheng, Jingyuan Duan, Ling Tian, Xia Wang, Lin Wang
2021 Wireless Communications and Mobile Computing  
The local differential privacy has been considered a novel paradigm for such distributed data publication.  ...  The data publication from multiple contributors has been long considered a fundamental task for data processing in various domains.  ...  U19A2059 and 61802050), the National Key R&D Program of China (No. 2018YFC0807500), and the Ministry of Science and Technology of Sichuan Province Program (Nos. 2018GZDZX0048 and 20ZDYF0343).  ... 
doi:10.1155/2021/6648775 fatcat:3aoekyz5nnbtho66xv4yooapdy

Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework [article]

Hongyu Li, Dan Meng, Hong Wang, Xiaolin Li
2020 arXiv   pre-print
We further clarify the relationship and differentiation between knowledge federation and other related research areas.  ...  With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence  ...  We deeply appreciate their unique contributions and great efforts in numerous ways in algorithms, systems, testing, and commercial deployment and production serving.  ... 
arXiv:2002.01647v3 fatcat:g3qnat45onappmzdrvzrfcj3w4

Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges [article]

Zhaohua Zheng, Yize Zhou, Yilong Sun, Zhang Wang, Boyi Liu, Keqiu Li
2021 arXiv   pre-print
With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem.  ...  In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields.  ...  Federated learning in the field of insurance Building a data service platform for the insurance industry requires integrating financial, medical, and other data from multiple parties.  ... 
arXiv:2102.01375v2 fatcat:sxlizo76sjexff3qmj5aueshse

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Jinbo Xiong, Rong Ma, Lei Chen, Youliang Tian, Li Lin, Biao Jin
2018 Wireless Communications and Mobile Computing  
How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users' data privacy, and prevent malicious users from providing false  ...  Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user's privacy.  ...  Acknowledgments This work is supported in part by the Natural Science Foundation of China (61772008, 61402109, 61502489, 61502248, and 61502102) and Guizhou Provincial Key Laboratory of Public Big Data  ... 
doi:10.1155/2018/8959635 fatcat:ja4osaioirdcvkzqnvqiuvixbe

CanDIG: Secure Federated Genomic Queries and Analyses Across Jurisdictions [article]

Lewis Jonathan Dursi, Zoltan Bozoky, Richard de Borja, Jimmy Li, David Bujold, Adam Lipski, Shaikh Farhan Rashid, Amanjeev Sethi, Neelam Memon, Dashaylan Naidoo, Felipe Coral-Sasso, Matthew Wong (+19 others)
2021 bioRxiv   pre-print
The Canadian Distributed Infrastructure for Genomics platform (CanDIG) enables federated querying and analysis of -omics and health data while keeping that data local and under local control.  ...  Data federation presents a solution to this, allowing for integration and analysis of large datasets from various sites while abiding by local policies.  ...  In local differential privacy, each data site determines its privacy parameter and adds the noise. Lower means lower privacy loss, requiring more noise and lower resulting accuracy.  ... 
doi:10.1101/2021.03.30.434101 fatcat:y6g4ic2pafghjnutw6rbyotuyq

Prochlo

Andrea Bittau, Bernhard Seefeld, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes
2017 Proceedings of the 26th Symposium on Operating Systems Principles - SOSP '17  
This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy.  ...  The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper)  ...  We thank Kunal Talwar for his help analyzing Stash Shuffle's security properties.  ... 
doi:10.1145/3132747.3132769 dblp:conf/sosp/BittauEMMRLRKTS17 fatcat:s5icmqnn6jgznjqhh4fo4fzahm

Incentive Design and Differential Privacy based Federated Learning: A mechanism design Perspective

Sungwook Kim
2020 IEEE Access  
To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives,  ...  FL is capable of significantly preserving end-users' private data from being exposed to external adversaries. However, private information can still be divulged by uploading parameters from users.  ...  To analyze the collected big data and obtain useful information for detection, classification, and prediction, traditional machine learning techniques need to aggregate massive user data with personal  ... 
doi:10.1109/access.2020.3030888 fatcat:vcqvjt77rzavrdjo34f6jx2pte

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges [article]

Bin Jiang, Jianqiang Li, Guanghui Yue, Houbing Song
2021 arXiv   pre-print
Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection.  ...  Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure.  ...  Wang et al. put forward a method for secure medical data collection based on local differential privacy [129] .  ... 
arXiv:2101.10569v2 fatcat:xwebjbcvcjhbbaehajhdfjocia

Federated learning for privacy-preserving data access

Małgorzata Śmietanka, Hirsh Pithadia, Philip Treleaven
2021 International Journal of Data Science and Big Data Analytics  
Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organizations.  ...  However, data privacy is becoming increasingly important because of regulations (e.g., EU GDPR) and the need to protect their sensitive and personal data.  ...  In the PPML context DP can be used at the local level (Local Differential Privacy, LDP). Each party perturbs their dataset and releases this obfuscated data for model training.  ... 
doi:10.51483/ijdsbda.1.2.2021.1-13 fatcat:b4rbxexaerburf5xrscizs27ae

Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy [article]

Chandra Thapa, Seyit Camtepe
2020 arXiv   pre-print
Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data.  ...  Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision  ...  There are two types of DP, namely local and global differential privacy.  ... 
arXiv:2008.10733v1 fatcat:oj2neoftf5hcbpatnfn7ntyhzy

Aggregated-Proofs Based Privacy-Preserving Authentication for V2G Networks in the Smart Grid

Hong Liu, Huansheng Ning, Yan Zhang, Laurence T. Yang
2012 IEEE Transactions on Smart Grid  
In addition, the aggregated pseudo-status variation is presented to realize that multiple BVs' power status can be collected as a whole without revealing any individual privacy.  ...  In AP3A, BVs are differentiated into either home or visiting mode, and multiple BVs can be simultaneously authenticated by an aggregator to conserve communication resources.  ...  In the home mode, multiple (i.e., ) simultaneously access to perform power services (e.g., charging). collects the BVs' power status data with the assistance of to provide information services for smart  ... 
doi:10.1109/tsg.2012.2212730 fatcat:ythvav6aprbrte2nbaj4tflsaa

Federated Learning for Healthcare Informatics [article]

Jie Xu and Benjamin S. Glicksberg and Chang Su and Peter Walker and Jiang Bian and Fei Wang
2020 arXiv   pre-print
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical  ...  the fragmented healthcare data sources with privacy-preservation.  ...  Acknowledgements The work is supported by ONR N00014-18-1-2585 and NSF 1750326. Conflict of interest The authors declare that they have no conflict of interest.  ... 
arXiv:1911.06270v2 fatcat:kvsrmvup4rhkxb2lp37tgoc22i
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