scholarly journals An Information-Theoretic Approach to Time-Series Data Privacy

2018 ◽  
Author(s):  
Yousef Amar ◽  
Hamed Haddadi ◽  
Richard Mortier
Keyword(s):  
Time Series ◽  
Data Privacy ◽  
Time Series Data ◽  
Series Data ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Information Theoretic Approach

scholarly journals An Information Theoretic Approach for Creating 3D Spatial Images from 4D Time Series Data

2017 ◽  
Vol 23 (S1) ◽  
pp. 100-101
Author(s):  
Willy Wriggers ◽  
Julio Kovacs ◽  
Federica Castellani ◽  
P. Thomas Vernier ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Information Theoretic Approach

scholarly journals SIMIT: Subjectively Interesting Motifs in Time Series

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 566 ◽  
Author(s):  
Junning Deng ◽  
Jefrey Lijffijt ◽  
Bo Kang ◽  
Tijl De Bie
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Synthetic Data ◽  
Series Data ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Novel Approach ◽  
Approximate Form ◽  
Real World Datasets ◽  
Information Theoretic Approach

Numerical time series data are pervasive, originating from sources as diverse as wearable devices, medical equipment, to sensors in industrial plants. In many cases, time series contain interesting information in terms of subsequences that recur in approximate form, so-called motifs. Major open challenges in this area include how one can formalize the interestingness of such motifs and how the most interesting ones can be found. We introduce a novel approach that tackles these issues. We formalize the notion of such subsequence patterns in an intuitive manner and present an information-theoretic approach for quantifying their interestingness with respect to any prior expectation a user may have about the time series. The resulting interestingness measure is thus a subjective measure, enabling a user to find motifs that are truly interesting to them. Although finding the best motif appears computationally intractable, we develop relaxations and a branch-and-bound approach implemented in a constraint programming solver. As shown in experiments on synthetic data and two real-world datasets, this enables us to mine interesting patterns in small or mid-sized time series.

Analysis of Privacy Preservation Techniques in IoT

2019 ◽  
Vol 10 (3) ◽  
pp. 27-33
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

scholarly journals Detection of Local Mixing in Time-Series Data Using Permutation Entropy

2021 ◽  
Author(s):  
Elizabeth Bradley ◽  
Michael Neuder ◽  
Joshua Garland ◽  
James White ◽  
Edward Dlugokencky
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ice Core ◽  
Series Data ◽  
Permutation Entropy ◽  
Information Theoretic ◽  
Mauna Loa ◽  
Model Free ◽  
Multiple Copies ◽  
Chemistry Experiment

<p>  While it is tempting in experimental practice to seek as high a  data rate as possible, oversampling can become an issue if one takes measurements too densely.  These effects can take many  forms, some of which are easy to detect: e.g., when the data sequence contains multiple copies of the same measured value.  In other situations, as when there is mixing—in the measurement apparatus and/or the system itself—oversampling effects can be harder to detect.  We propose a novel, model-free technique to detect local mixing in time series using an information-theoretic technique called permutation entropy.  By varying the temporal resolution of the calculation and analyzing the patterns in the results, we can determine whether the data are mixed locally, and on what scale.  This can be used by practitioners to choose appropriate lower bounds on scales at which to measure or report data.  After validating this technique on several synthetic examples, we demonstrate its effectiveness on data from a chemistry experiment, methane records from Mauna Loa, and an Antarctic ice core.</p>

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