Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

9.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
9.0K
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

173
A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
173
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.1K
Differential Leveling01:12

Differential Leveling

949
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
949
Separable Differential Equations01:20

Separable Differential Equations

365
A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...
365
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

659
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
659

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Visualization and detection of ciliary beating frequency in human airway organoids based on image correlated methods for drug screening.

Biochemical and biophysical research communications·2026
Same author

Microbial Degradation of Plastics: Mechanisms, Pathways, and Multiomics Insights.

Environmental science & technology·2026
Same author

Interplay between micro-nanoplastics and dissolved organic matter in aquatic ecosystems: From eco-corona dynamics to microalgal adaptive resilience.

Journal of hazardous materials·2026
Same author

Interactions between dissolved organic matter of different molecular weights and hexabromocyclododecanes.

Marine environmental research·2026
Same author

RNA methylation in urological cancers: regulatory logic, biological functions, and clinical relevance.

Frontiers in immunology·2026
Same author

Functional drug screening of tumor organoids on an active-matrix digital microfluidic chip for cancer precision medicine.

Microsystems & nanoengineering·2026

Related Experiment Video

Updated: May 5, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K

Sequential Change Detection with Local Differential Privacy.

Lixing Zhang1, Xuran Liu2, Ruizhi Zhang2

  • 1Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455, USA.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

We developed a privacy-preserving change detection method, Local Differential Privacy CUSUM (LDP-CUSUM), for scenarios where data cannot be shared. This method balances data privacy with efficient change detection performance.

Keywords:
average run lengthcumulative sum testdetection delaylocal differential privacysequential change detection

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

16.5K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Related Experiment Videos

Last Updated: May 5, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

16.5K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Area of Science:

  • Statistics
  • Signal Processing
  • Data Privacy

Background:

  • Sequential change detection is crucial in statistics and signal processing.
  • The CUSUM procedure is optimal for known distributions but requires raw data access.
  • Data privacy concerns often prevent direct sharing of raw observations.

Purpose of the Study:

  • To introduce a novel change detection method that enforces local differential privacy.
  • To analyze the performance trade-offs between privacy, false alarm rate, and detection delay.
  • To demonstrate the efficacy of the proposed method in various settings.

Main Methods:

  • Developed Local Differential Privacy CUSUM (LDP-CUSUM) by applying local DP to raw data before CUSUM analysis.
  • Derived closed-form bounds for average run length to false alarm and worst-case average detection delay.
  • Conducted numerical simulations and a real-data case study for validation.

Main Results:

  • LDP-CUSUM effectively detects changes while preserving data privacy at the source.
  • Closed-form bounds explicitly characterize the trade-off between privacy level, false alarm rate, and detection efficiency.
  • The method shows strong detection performance across diverse scenarios.

Conclusions:

  • LDP-CUSUM offers a practical solution for privacy-preserving sequential change detection.
  • The derived bounds provide valuable insights for tuning the method's parameters.
  • This approach enables robust change detection in sensitive data environments.