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Local Differential Privacy and Its Applications: A Comprehensive Survey
[article]
2020
arXiv
pre-print
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This survey provides a comprehensive and structured overview of the local differential privacy technology. ...
We discuss the practical deployment of local differential privacy and explore its application in various domains. ...
Distributed environment makes it even more challenging. Naive Bayes classification and decision tree are two popular and simple supervised learning algorithms. ...
arXiv:2008.03686v1
fatcat:l7z3gip2ivdmvin7lraxd4vciy
On the Differential Privacy of Bayesian Inference
2016
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Worked examples and experiments with Bayesian naive Bayes and Bayesian linear regression illustrate the application of our mechanisms. ...
We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. ...
This work was partially supported by the Swiss National Foundation grant "Swiss Sense Synergy" CRSII2-154458, and by the Australian Research Council (DE160100584). ...
doi:10.1609/aaai.v30i1.10254
fatcat:ivsl7lh45rgu3fhrpqqhfijn3y
On the Differential Privacy of Bayesian Inference
[article]
2015
arXiv
pre-print
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Worked examples and experiments with Bayesian naïve Bayes and Bayesian linear regression illustrate the application of our mechanisms. ...
We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. ...
This allows us to assume a minimal probability ω assigned to any sub-event in the naïve Bayes network, so that the joint distribution satisfies Assumption 1. ...
arXiv:1512.06992v1
fatcat:lq45w6gmqrbsznrkbr2v47jmje
Local Privacy-preserving Mechanisms and Applications in Machine Learning
[article]
2024
arXiv
pre-print
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The emergence and evolution of Local Differential Privacy (LDP) and its various adaptations play a pivotal role in tackling privacy issues related to the vast amounts of data generated by intelligent devices ...
The core principle of LDP lies in its technique of altering each user's data locally at the client end before it is sent to the server, thus preventing privacy violations at both stages. ...
Similarly, Xue et al. [45] also focused on training a Naïve Bayes classifier with LDP, utilizing joint distributions to compute conditional distributions. ...
arXiv:2401.13692v1
fatcat:mkozeddjdfcf7elc5vtmy6gpdm
Multiple Disease Diagnosis using Two Layer Machine Learning Approach
2020
International Journal for Research in Applied Science and Engineering Technology
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The focus of this project is to aid and help a medical professional to verify and diagnose the patient with certainty using the symptoms provide by them. ...
This system will be free of any biases and diagnose solely based on factual data. There is a need of a remote diagnosis system. ...
Differential distributions of services, power, and resources have caused difference in healthcare access. ...
doi:10.22214/ijraset.2020.6183
fatcat:yu4qaa2rurfvnc4cav4ut7xqzy
$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy
2018
IEEE Transactions on Information Forensics and Security
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Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. ...
Then, we develop a Local differentially private high-dimensional data Publication algorithm, LoPub, by taking advantage of our distribution estimation techniques. ...
Hence, local differential privacy (LDP) [9] , [13] , [23] , [24] has been proposed to provide individual privacy guarantees for distributed users. ...
doi:10.1109/tifs.2018.2812146
fatcat:ytjik6agc5bj7lwyyolkr475fa
Directed Information as Privacy Measure in Cloud-based Control
[article]
2017
arXiv
pre-print
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In order to address privacy concerns in such a control architecture, we first investigate the issue of finding an appropriate privacy measure for clients who desire to keep local state information as private ...
We consider cloud-based control scenarios in which clients with local control tasks outsource their computational or physical duties to a cloud service provider. ...
By Postulate 3, we need to characterize the privacy leakage function for X t due to disclosing Y t under the joint distribution P X t ,Yt|y t−1 ,u t−1 . ...
arXiv:1705.02802v1
fatcat:nwxib3ghpfe3zc7b46qmpozdu4
An Efficient Bayes Classifiers Algorithm for Traceability of Food Supply Chain Management using Internet of Things
2019
International Journal of Engineering and Advanced Technology
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So the proposed efficient Bayes Classifiers Algorithm will be capable to overcome all provocation of conventional supply chain and afford secure background and food safety for FSCM process using IoT technology ...
A challenging assignment in today's food industry is distributing the high quality of foods throughout the supply chain management. ...
And in the end, it shows how the management of Multinomial Naïve Bayes can be improved using locally subjective learning [35] . ...
doi:10.35940/ijeat.a1379.109119
fatcat:u3uw6qgngjd7zcu7ud6x4gv2ae
DP-SMOTE: Integrating Differential Privacy and Oversampling Technique to Preserve Privacy in Smart Homes
2024
Al-Azhar Bulletin of Science
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However, sharing such data necessitates privacy-preserving practices. This paper introduces a robust method for secure sharing of data to service providers, grounded in differential privacy (DP). ...
Additionally, it shows a high classification accuracy, ranging from 90% to 98%, across various classification techniques. ...
Naive Bayes Classifier (NB): These algorithms apply Bayes' theorem under the assumption of conditional independence between feature pairs, given a class variable. ...
doi:10.58675/2636-3305.1669
fatcat:rr4qtkatvfak7op7lmsxt6zdt4
A Comprehensive Survey on Local Differential Privacy
2020
Security and Communication Networks
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., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. ...
Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while ...
[63] proposed a novel Consistent Adaptive Local Marginal (CALM) method for computing any k-way marginal (joint distribution) under the local setting of differential privacy. Peng et al. ...
doi:10.1155/2020/8829523
fatcat:xjk3vgyambb5xioc2q5hyr2hua
LTU Attacker for Membership Inference
2022
Algorithms
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We prove that, under certain conditions, even a "naïve" LTU Attacker can achieve lower bounds on privacy loss with simple attack strategies, leading to concrete necessary conditions to protect privacy, ...
The Defender aims at optimizing a dual objective: utility and privacy. ...
Acknowledgments: We are grateful to our colleagues Kristin Bennett and Jennifer He for stimulating discussion.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/a15070254
fatcat:ijja6crsc5eehmk34ik4mhbghm
Data Privacy Preservation on the Internet of Things
[article]
2023
arXiv
pre-print
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This growth in the number of IoT devices and successful IoT services has generated a tremendous amount of data. However, this humongous volume of data poses growing concerns for user privacy. ...
This paradigm, known as the Internet of Things (IoT) is progressing quickly with an estimated 27 billion devices by 2025. ...
Bost protocols for privacy-preserving classification using different datasets and models including hyperplane decision, naive Bayes, decision trees, support vector machines, multi-layer extreme learning ...
arXiv:2304.00258v1
fatcat:hr56nh7jwfeftgsy25iwfe53ni
Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability
2022
Sensors
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The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality ...
The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain—namely security and safety, asset localization, quality control, ...
Classification on network data, Clustering [25,46-51] Privacy leaking Differential privacy and federated learning [52-54] Data integrity Latent space methods (Boltzmann-machine, DBN), Classification (Random ...
doi:10.3390/s22239148
pmid:36501848
pmcid:PMC9739236
fatcat:c4n5bagqefagbmhk5yaxfctroa
A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis
2020
Sensors
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Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing ...
Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. ...
[124] proposed to train a Naïve Bayes classifier with LDP. Naïve Bayes classification is to find the most probable label when given a new instance. ...
doi:10.3390/s20247030
pmid:33302517
pmcid:PMC7763193
fatcat:25iufaivynabdftrzq4rzxsz2e
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis in Crowdsensing
[article]
2020
arXiv
pre-print
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Other Versions
Local differential privacy (LDP) has been proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting ...
Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. ...
[124] proposed to train a Naïve Bayes classifier with LDP. Naïve Bayes classification is to find the most probable label when given a new instance. ...
arXiv:2010.05253v2
fatcat:uuts5enifreixjt4lf6yjrepl4
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