WiMi Announced a Secure and Trusted Collaborative Learning Based on Blockchain for IoT

Rhea-AI Summary

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) has announced a blockchain-based trusted collaborative learning (TrusCL) framework for IoT. This framework combines Homomorphic Encryption (HE) and Differential Privacy (DP) techniques to address privacy and computational challenges in AIoT environments. The solution allows for secure model training on sensitive data without direct access to the data itself.

Key features of the TrusCL framework include:

• Enhanced security through blockchain technology
• Tamper-proof recording of model updates and data contributions
• Smart contracts to enforce predefined rules
• Dynamic adjustment of participant contributions and rewards

This innovative approach aims to improve security, efficiency, and trustworthiness in collaborative learning for AIoT applications, supporting the development of Industry 4.0.

Positive

• Introduction of a secure and trusted collaborative learning framework for IoT
• Integration of Homomorphic Encryption and Differential Privacy techniques for enhanced data protection
• Implementation of blockchain technology for improved transparency and security
• Support for dynamic adjustment of participant contributions and rewards

Negative

• None.

BEIJING, Aug. 6, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced a blockchain-based trusted collaborative learning (TrusCL) framework for IoT which is a solution for the privacy-preserving and computational requirements of collaborative learning in AIoT environments. The TrusCL framework is combined with Homomorphic Encryption (HE) and Differential Privacy (DP) techniques, both of which are significant in the field of data protection and privacy enhancement.

Homomorphic encryption allows direct computation on encrypted data without the need to decrypt it first, which means that the data can be processed in an encrypted state, thus preventing data leakage at the source. This provides strong technical support for data privacy protection, allowing data owners to participate in the model training process while keeping data private. The differential privacy technique, on the other hand, further strengthens privacy by adding random noise to the dataset to ensure that even with an external query, no information about any particular individual can be accurately inferred. Combining homomorphic encryption and differential privacy, the TrusCL framework achieves a balance between privacy protection and model learning efficiency, which ensures the quality of model training and maintains the data privacy of the participants.

WiMi also added another layer of security by introducing blockchain technology into the TrusCL framework. All key activities of collaborative learning, including model updates, data contribution proofs and computation processes, will be recorded on the blockchain in a tamper-proof manner. This transparency and traceability effectively inhibit dishonest behavior, and any attempts to manipulate data or computation results will be promptly detected and blocked. The blockchain's smart contracts also automatically enforce the terms of the agreement, ensuring that the participants' computational behavior conforms to predefined rules, thus promoting a fair, transparent and trustworthy collaborative environment.

For model demanders, the blockchain-based IoT secure and trustworthy collaborative learning framework provides a secure and efficient platform that enables them to use sensitive data to securely train machine learning models without direct access to that data. The framework also supports dynamic adjustment of participants' contribution and reward mechanisms to incentivize the contribution of more high-quality data and computational resources, further improving the accuracy and generalization of model training.

The WiMi's blockchain-based secure and trustworthy collaborative learning framework for IoT opens a path to higher security and trustworthiness for collaborative learning in the AIoT era through cutting-edge technology integration. With the in-depth application and continuous optimization of this framework, data sharing and intelligent analysis in AIoT will be more secure and efficient in the future, which will lay a solid foundation for the deepening development of Industry 4.0.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

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SOURCE WiMi Hologram Cloud Inc.

FAQ

What is WiMi's new blockchain-based framework for IoT called?

WiMi's new blockchain-based framework for IoT is called TrusCL (Trusted Collaborative Learning).

How does the TrusCL framework enhance data privacy in IoT collaborative learning?

The TrusCL framework enhances data privacy by combining Homomorphic Encryption (HE) and Differential Privacy (DP) techniques, allowing computation on encrypted data and adding random noise to datasets to prevent individual data inference.

What role does blockchain play in WiMi's TrusCL framework for IoT?

Blockchain technology in the TrusCL framework provides tamper-proof recording of model updates and data contributions, enforces smart contracts, and creates a transparent and trustworthy collaborative environment.

How does WiMi's TrusCL framework benefit model demanders in IoT applications?

The TrusCL framework allows model demanders to securely train machine learning models using sensitive data without direct access to that data, while also incentivizing high-quality data contributions through dynamic reward mechanisms.