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

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

Security Audit of IoT Device Networks: A Reproducible Machine Learning Framework for Threat Detection and Performance Benchmarking.

Sensors (Basel, Switzerland)·2025
Same author

Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography.

Sensors (Basel, Switzerland)·2024
Same author

Optimization of a Simulated Annealing Algorithm for S-Boxes Generating.

Sensors (Basel, Switzerland)·2022
Same author

Direct Spread Spectrum Technology for Data Hiding in Audio.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: May 5, 2026

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

1.6K

Performance Evaluation of zk-SNARK Protocols for Privacy-Preserving Sensor Data Verification: A Systematic

Oleksandr Kuznetsov1,2, Yelyzaveta Kuznetsova3, Gulzat Ziyatbekova4,5,6

  • 1Department of Theoretical and Applied Sciences (DISTA), eCampus University, Via Isimbardi 10, 22060 Novedrate, Italy.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) provide privacy-preserving data verification for sensor networks. Benchmarking the Groth16 protocol shows constant proof size and near-constant verification time, enabling practical deployment.

Keywords:
Groth16IoT securityblockchainperformance benchmarkingprivacy-preserving verificationsensor networkszero-knowledge proofszk-SNARK

More Related Videos

Technical Aspect of the Automated Synthesis and Real-Time Kinetic Evaluation of [11C]SNAP-7941
09:50

Technical Aspect of the Automated Synthesis and Real-Time Kinetic Evaluation of [11C]SNAP-7941

Published on: April 28, 2019

9.0K
Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.3K

Related Experiment Videos

Last Updated: May 5, 2026

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

1.6K
Technical Aspect of the Automated Synthesis and Real-Time Kinetic Evaluation of [11C]SNAP-7941
09:50

Technical Aspect of the Automated Synthesis and Real-Time Kinetic Evaluation of [11C]SNAP-7941

Published on: April 28, 2019

9.0K
Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.3K

Area of Science:

  • Cryptography
  • Computer Science
  • Network Security

Background:

  • Sensor networks in critical infrastructure, healthcare, and smart cities require privacy-preserving data verification.
  • Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are a promising cryptographic tool for this purpose.
  • The practical deployment feasibility of zk-SNARKs in resource-constrained sensor environments is not well understood.

Purpose of the Study:

  • To systematically benchmark the Groth16 zk-SNARK protocol's performance across diverse computational complexities.
  • To evaluate the feasibility of using Groth16 for privacy-preserving data verification in sensor networks.
  • To provide actionable guidance for designing future sensor network architectures.

Main Methods:

  • Benchmarking the Groth16 zk-SNARK protocol using an automated, open-source framework (Circom-snarkjs).
  • Testing across eight representative circuit types with computational complexities ranging from 1 to 1,510,185 constraints.
  • Conducting 160 statistically controlled measurements, collecting data on proof generation time, verification time, proof size, memory, and witness generation overhead.

Main Results:

  • Groth16 proofs exhibit a constant size (804.7±1.7 bytes) and near-constant verification time (0.662±0.032 s) irrespective of circuit complexity.
  • Proof generation time scales sub-linearly (α=0.256, R²=0.608) with computational complexity.
  • Statistically significant differences in performance were observed across circuit categories (ANOVA: F=355.0, p<10⁻⁷⁹, η²=0.94).

Conclusions:

  • The Groth16 zk-SNARK protocol is practically feasible for privacy-preserving data verification in sensor networks due to its consistent proof size and verification time.
  • Identified three operational deployment tiers and estimated energy budgets for battery-powered devices.
  • Findings offer crucial guidance for designing next-generation, privacy-preserving sensor network systems.