Jove
Visualize
Contact Us

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
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...
6.4K
Censoring Survival Data01:09

Censoring Survival Data

108
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
108
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.8K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.8K
Ethical Standards II01:23

Ethical Standards II

684
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
684
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Differential privacy protection method based on published trajectory cross-correlation constraint.

PloS one·2020
See all related articles
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 Experiment Video

Updated: Jul 12, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K

Research on differential privacy protection method based on user tendency.

Zhaowei Hu1,2

  • 1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China.

Plos One
|October 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel privacy-preserving method to protect user tendency data. It quantifies user behavior using Markov chains and applies differential privacy to enhance data security and availability.

More Related Videos

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

9.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Jul 12, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K
Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

9.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Computer Science
  • Data Privacy
  • Machine Learning

Background:

  • Current privacy-preserving methods struggle with user tendency, leading to privacy leakage.
  • Mining user activity rules from large datasets presents significant privacy challenges.

Purpose of the Study:

  • To propose an extended differential privacy method that addresses privacy leakage from user tendency.
  • To accurately measure and protect user propensity privacy while maintaining data utility.

Main Methods:

  • Constructed a Markov chain to represent user tendency as measurable state transition probabilities.
  • Developed an extended (P,ε)-differential privacy protection method incorporating a privacy model parameter R.
  • Quantified user propensity probability and combined it with differential privacy budget for dynamic noise addition.

Main Results:

  • Transformed qualitative user tendency descriptions into a quantitative representation for accurate measurement.
  • Successfully protected user propensity privacy information through dynamic noise adjustment.
  • Demonstrated the feasibility and effectiveness of the proposed method via experimental validation.

Conclusions:

  • The proposed extended differential privacy method effectively protects user propensity privacy.
  • The method balances privacy protection with improved data availability.
  • This approach offers a robust solution for privacy-preserving analysis of user activity data.