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

Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

364
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
364
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

314
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
314
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

9.8K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
9.8K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

303
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...
303
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

98
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
98
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

823
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
823

You might also read

Related Articles

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

Sort by
Same author

Flexible Threshold Quantum Homomorphic Encryption on Quantum Networks.

Entropy (Basel, Switzerland)·2025
Same author

An MLWE-Based Cut-and-Choose Oblivious Transfer Protocol.

Entropy (Basel, Switzerland)·2024
Same author

Redactable Blockchain Trust Scheme Based on Reputation Consensus for MEC.

Computational intelligence and neuroscience·2022
Same author

Efficient Linkable Ring Signature Scheme over NTRU Lattice with Unconditional Anonymity.

Computational intelligence and neuroscience·2022
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
See all related articles

Related Experiment Video

Updated: Aug 27, 2025

Trapping of Micro Particles in Nanoplasmonic Optical Lattice
07:20

Trapping of Micro Particles in Nanoplasmonic Optical Lattice

Published on: September 5, 2017

6.6K

Efficient Lattice-Based Ring Signature Scheme without Trapdoors for Machine Learning.

Qing Ye1, Yongkang Lang1, Zongqu Zhao1

  • 1School of Software, Henan Polytechnic University, Jiaozuo 454000, China.

Computational Intelligence and Neuroscience
|September 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel lattice-based ring signature (RS) scheme without trapdoors, enhancing privacy protection for machine learning (ML). The new scheme offers superior computational efficiency and stronger security guarantees, making it ideal for ML applications.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

673

Related Experiment Videos

Last Updated: Aug 27, 2025

Trapping of Micro Particles in Nanoplasmonic Optical Lattice
07:20

Trapping of Micro Particles in Nanoplasmonic Optical Lattice

Published on: September 5, 2017

6.6K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

673

Area of Science:

  • Cryptography
  • Lattice-based cryptography
  • Machine Learning Security

Background:

  • Machine learning (ML) and privacy protection are deeply intertwined, with ML systems being both targets and tools for privacy.
  • Ring signatures (RS) offer cryptographic privacy, and lattice-based RS provides quantum resistance.
  • Existing lattice-based RS schemes often rely on trapdoors, hindering computational efficiency due to hidden algebraic structures.

Purpose of the Study:

  • To construct a novel, efficient, and secure lattice-based ring signature (RS) scheme for machine learning (ML) scenarios.
  • To develop an RS scheme that avoids the computational overhead associated with trapdoor constructions.
  • To enhance privacy protection in ML by leveraging advanced cryptographic techniques.

Main Methods:

  • Utilized the Lyubashevsky collision-resistant hash function over lattices.
  • Constructed a ring signature scheme based on ideal lattices.
  • Employed the Fiat‒Shamir with aborts (FSwA) protocol for the construction.
  • Analyzed security in terms of unconditional anonymity against chosen setting attacks (UA-CSA) and unforgeability with respect to insider corruption (EU-IC).

Main Results:

  • Proposed a new lattice-based RS scheme without trapdoors.
  • Achieved unconditional anonymity against chosen setting attacks (UA-CSA), a stronger security level than anonymity against full key exposure (anonymity-FKE).
  • Satisfied unforgeability with respect to insider corruption (EU-IC).
  • Demonstrated superior computational efficiency in signing and verification compared to existing schemes with similar security levels.

Conclusions:

  • The developed lattice-based RS scheme offers enhanced privacy and security for ML applications.
  • The absence of trapdoors and the use of ideal lattices lead to significant improvements in computational efficiency.
  • This scheme is well-suited for ML environments requiring robust privacy protection and high performance.