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Continuous Release of Data Streams under both Centralized and Local Differential Privacy
[article]
2020
arXiv
pre-print
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In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). ...
Within our framework, we design an algorithm satisfying the more stringent setting of DP called local DP (LDP). To our knowledge, this is the first LDP algorithm for publishing streaming data. ...
To summarize, the main contributions of this paper are threefold: • We design ToPS for releasing real-time data streams under differential privacy. ...
arXiv:2005.11753v1
fatcat:3lugkw7xmfaklgjbhmivczfvle
Mobile Privacy-Preserving Crowdsourced Data Collection in the Smart City
[article]
2016
arXiv
pre-print
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However, the streaming of big data from IoT devices, especially from mobile platforms like pedestrians and cars, raises significant privacy concerns. ...
This work proposes a scalable smart city privacy-preserving architecture named Authorized Analytics that enables each node (e.g. vehicle) to divulge (contextually) local privatised data. ...
To ensure timely results support for stream analytics is desirable. This ensures that data is processed continuously and released on the order of minutes. ...
arXiv:1607.02805v1
fatcat:pvwb76e5ifb7za5f7nchorm4ga
Differentially Private Robust Low-Rank Approximation
2018
Neural Information Processing Systems
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We propose an algorithm that guarantees We study extensions to the streaming setting where entries of the matrix arrive in an arbitrary order and output is produced at the very end or continually. ...
k (A) + ⌧, where kBk p is the entry-wise 'p-norm of B and OPT k (A) := min rank(X)k kA Xk p . ...
Acknowledgements This research was supported in part by NSF BIGDATA grant IIS-1546482, NSF BIGDATA grant IIS-1838139, NSF Career CCF-1652257, and ONR Award N00014-18-1-2364. ...
dblp:conf/nips/AroraBU18
fatcat:25k3feqvnvduvjt5v2vdbnypfu
Privacy in Information-Rich Intelligent Infrastructure
[article]
2017
arXiv
pre-print
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Intelligent infrastructure will critically rely on the dense instrumentation of sensors and actuators that constantly transmit streaming data to cloud-based analytics for real-time monitoring. ...
meter data, resulting not only in loss of privacy but potentially also putting us at risk. ...
Acknowledgements Thanks to Rakesh Vohra, Aaron Roth, and Andreas Haeberlen at the University of Pennsylvania for discussions on the privacy of IoT data, and to Helen Wright for invaluable help in writing ...
arXiv:1706.01985v1
fatcat:mbu7xn2honhndimxgp5t6mz434
PAS-MC: Privacy-preserving Analytics Stream for the Mobile Cloud
[article]
2016
arXiv
pre-print
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This paper introduces PAS-MC, the first practical privacy-preserving and anonymity stream analytics system. ...
In today's digital world, personal data is being continuously collected and analyzed without data owners' consent and choice. ...
to the local differential privacy mechanism. ...
arXiv:1604.04892v1
fatcat:7jlfkxxscngindcmbj2ybidgri
Pan-Private Uniformity Testing
[article]
2020
arXiv
pre-print
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In contrast, a locally differentially private algorithm may only access data through public interaction with data holders, and this interaction must be a differentially private function of the data. ...
Unlike a centrally private algorithm, the algorithm receives data one element at a time and must maintain a differentially private internal state while processing this stream. ...
These improved the presentation, statements, and proofs of several of our results. ...
arXiv:1911.01452v3
fatcat:phe2floccvgj5ogfbabygxwfci
A One-Pass Private Sketch for Most Machine Learning Tasks
[article]
2020
arXiv
pre-print
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Inspired by recent progress toward general-purpose data release algorithms, we propose a private sketch, or small summary of the dataset, that supports a multitude of machine learning tasks including regression ...
Our sketch consists of randomized contingency tables that are indexed with locality-sensitive hashing and constructed with an efficient one-pass algorithm. ...
We use the MatLab code released by [26] and report the mean squared error (MSE) on a held-out test set in Figure 4 . ...
arXiv:2006.09352v1
fatcat:btryq2ftjnad7jyasj7wqrx2oi
Mobility Data Analysis and Applications: A mid-year 2021 Survey
[article]
2021
arXiv
pre-print
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In this work we review recent works analyzing mobility data and its application in understanding the epidemic dynamics for the COVID-19 pandemic and more. ...
We also discuss privacy-preserving solutions to analyze the mobility data in order to expand its reach towards a wider population. ...
There are variants of differential privacy that operate under different threat models like local differential privacy [35] that do not require a trusted and centralized aggregator of the data sources ...
arXiv:2109.07901v1
fatcat:3cz7cyw42bagldq4eax4jdcddq
Application of Personal Information Privacy Protection Based on Machine Learning Algorithm
2022
Computational Intelligence and Neuroscience
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parameter updating strategy of joint machine learning under privacy protection are designed. ...
And, means and reference value is provided for the development direction of privacy protection. ...
A local differential privacy data stream protection protocol is proposed that can provide data stream privacy while also ensuring high data availability and requiring less storage and computing power. ...
doi:10.1155/2022/6710631
pmid:35958767
pmcid:PMC9357731
fatcat:6bgvh25f6bh7vcwcqkqfkbcsji
An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams
2019
Computers & security
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2019 pre-print arXiv:1907.13498v1 fatcat:hexvgar7cfbvbbx2og2y5cha34
We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides ...
The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. ...
Local Differential Privacy Global differential privacy (GDP) and local differential privacy (LDP) are the two main approaches to differential privacy. ...
doi:10.1016/j.cose.2019.101570
fatcat:x57ucwcgibb4pf3qgcn7cditpu
DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data
2020
IEEE Internet of Things Journal
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2020 pre-print arXiv:2009.14125v1 fatcat:aecbyozetrconkvj2fwmrlctda
Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowd-sourcing participants. ...
Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multi-dimensional infinite crowd-data streams. ...
Nonetheless, besides privacy concerns, continuous multi-hop broadcast incurs both tremendous communication overhead and high delay. • Real-time data release. ...
doi:10.1109/jiot.2020.3020089
fatcat:qwycqcrhkjai3gk2tnnndkznzy
Security and privacy for big data: A systematic literature review
2016
2016 IEEE International Conference on Big Data (Big Data)
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With big data processing, analyses can be carried out on huge amounts of user data. ...
In order to have access to real world use cases, we have studied privacy-preserving big data analysis in the context of the automotive domain. ...
Thus, vehicular data is comprised of both dynamic, continuously growing, data as well as static data. ...
doi:10.1109/bigdata.2016.7841037
dblp:conf/bigdataconf/NelsonO16
fatcat:uiippetep5ea3ote2gzsxd5acm
PREDICT: Privacy and Security Enhancing Dynamic Information Collection and Monitoring
2013
Procedia Computer Science
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The overall aim of the project is to develop a framework with algorithms and mechanisms for privacy and security enhanced dynamic data collection, aggregation, and analysis with feedback loops. ...
In this paper, we present an overview of our ongoing project PREDICT (Privacy and secuRity Enhancing Dynamic Information Collection and moniToring). ...
Acknowledgement This research is supported by the Air Force Office of Scientific Research (AFOSR) DDDAS program under grant FA9550-12-1-0240. ...
doi:10.1016/j.procs.2013.05.367
fatcat:5qchd5jl2vhkzdysuqpghyo2pu
Efficient data perturbation for privacy preserving and accurate data stream mining
2018
Pervasive and Mobile Computing
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2018 pre-print arXiv:1806.06151v1 fatcat:5ejgqx7bzrajdld336r2o23fquOther Versions
Existing privacy preservation methods cannot provide a good balance between data utility and privacy, and also have problems with efficiency and scalability. ...
P^2RoCAl offers better data utility than similar methods: classification accuracies of P^2RoCAl perturbed data streams are very close to those of the original data streams. ...
Global differential privacy (GDP) and local differential privacy (LDP) are the two models that can be used to achieve DP over data. ...
doi:10.1016/j.pmcj.2018.05.003
fatcat:6qai6rh3dzf3lnr4gty6pllxcq
Private Graph Data Release: A Survey
[article]
2022
arXiv
pre-print
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of the limitations of Differential Privacy. ...
Many of these mechanisms are natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that address some ...
The proposed approach first identifies a small number of important data points from an entire data stream, perturbs these points under local differential privacy, and then reports the perturbed data to ...
arXiv:2107.04245v2
fatcat:54bvnswpnbfffiqd5ee5opfope
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