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WiMi Announced a Blockchain Data Encryption Technology Based on Machine Learning and Fully Homomorphic Encryption Algorithm

WiMi Announced a Blockchain Data Encryption Technology Based on Machine Learning and Fully Homomorphic Encryption Algorithm

WiMi宣佈推出基於機器學習和完全同態加密算法的區塊鏈數據加密技術
PR Newswire ·  05/23 00:54

BEIJING, May. 22, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that the blockchain data encryption based on machine learning and fully homomorphic encryption algorithm is a comprehensive solution which applies cutting-edge cryptography and artificial intelligence technologies for blockchain data protection. It combines the intelligent key management of machine learning and the direct ciphertext computation capability of fully homomorphic encryption, aiming to ensure that the data on the blockchain achieves effective protection of sensitive information while maintaining a high degree of transparency and tamper-proofness.

北京,2024年5月22日 /PRNewswire/ — 全球領先的全息增強現實(“AR”)技術提供商WiMi Hologram Cloud Inc.(納斯達克股票代碼:WIMI)(“WiMi” 或 “公司”)今天宣佈,基於機器學習和完全同態加密算法的區塊鏈數據加密是一種綜合解決方案,應用尖端的密碼學和人工智能技術來保護區塊鏈數據。它結合了機器學習的智能密鑰管理和完全同態加密的直接密文計算能力,旨在確保區塊鏈上的數據在保持高度的透明度和防篡改性的同時,實現對敏感信息的有效保護。

Full homomorphic encryption (FHE), as an advanced cryptographic technique, can perform arithmetic operations on encrypted data without first decrypting it, the result of the computation remains encrypted, and the decrypted result is the same as the result of the direct computation on the plaintext. This technology provides new ideas for solving blockchain privacy issues. FHE can also support more complex operations, such as exponentiation, division, comparison, etc., making it possible to execute machine learning models on encrypted data. By fully homomorphic encryption of sensitive data on the blockchain (e.g., transaction amounts, user identities, smart contract parameters, etc.), it ensures that while this information is open and transparent on the chain, only the data owner or authorized participants can decrypt and access the specific content, realizing the harmonious coexistence of privacy protection and the principle of blockchain transparency. After receiving the encrypted data, blockchain nodes can directly perform operations such as verification, bookkeeping, and smart contract execution on the ciphertext. FHE ensures that these operations do not expose plaintext information and the computation results remain encrypted. The data owner or the participant with the corresponding rights uses the private key to decrypt the encrypted results to make decisions, transfer assets, and confirm the results of contract execution. Unauthorized users cannot decrypt thus protecting data privacy.

全同態加密 (FHE) 作爲一種先進的加密技術,無需事先解密即可對加密數據執行算術運算,計算結果保持加密狀態,解密結果與在明文上直接計算的結果相同。這項技術爲解決區塊鏈隱私問題提供了新思路。FHE 還可以支持更復雜的操作,例如指數運算、除法、比較等,從而可以在加密數據上執行機器學習模型。通過對區塊鏈上的敏感數據(例如交易金額、用戶身份、智能合約參數等)進行完全同態加密,它確保了這些信息在鏈上公開透明,但只有數據所有者或授權參與者才能解密和訪問特定內容,從而實現隱私保護與區塊鏈透明原則的和諧共存。收到加密數據後,區塊鏈節點可以直接對密文進行驗證、記賬和智能合約執行等操作。FHE 確保這些操作不會暴露純文本信息,並且計算結果保持加密狀態。數據所有者或具有相應權限的參與者使用私鑰解密加密結果,以做出決策、轉移資產並確認合同執行結果。未經授權的用戶無法解密,因此可以保護數據隱私。

The application of machine learning technology in information security is also expanding, especially in key management, threat detection, risk assessment, etc. With algorithm optimization and hardware acceleration, machine learning models are able to efficiently process large amounts of data in a real-time environment, analyze multi-dimensional information such as the network environment, user behavior, blockchain transaction patterns, etc. in real time, dynamically generate and update encryption keys, improve the randomness and anti-cracking ability of the keys, and realize the intelligent management of encryption systems. Using the dynamic key generated by machine learning to encrypt sensitive data on the blockchain can ensure the security of the data when it is broadcast and stored on the chain. At the same time, machine learning can also carry out a risk assessment and early warning of the blockchain system, adjust the encryption strategy according to the risk posture, enhance the adaptability and defense capability of data encryption, cope with changing means of attack and security threats, and ensure data security.

機器學習技術在信息安全中的應用也在擴展,特別是在密鑰管理、威脅檢測、風險評估等方面,通過算法優化和硬件加速,機器學習模型能夠在實時環境中高效處理大量數據,實時分析網絡環境、用戶行爲、區塊鏈交易模式等多維度信息,動態生成和更新加密密鑰,提高密鑰的隨機性和防破解能力,並實現加密系統的智能管理。使用機器學習生成的動態密鑰對區塊鏈上的敏感數據進行加密,可以確保數據在廣播和存儲在鏈上時的安全。同時,機器學習還可以對區塊鏈系統進行風險評估和預警,根據風險態勢調整加密策略,增強數據加密的適應性和防禦能力,應對不斷變化的攻擊手段和安全威脅,確保數據安全。

WiMi's data encryption technology based on machine learning and fully homomorphic encryption algorithm can be utilized in blockchain in scenarios including privacy-protected transactions, private smart contracts, cross-chain data exchange and collaboration, on-chain data analysis and machine learning. For example, both parties to a transaction can use FHE to encrypt sensitive information such as transaction amount, asset type, and purpose of the transaction, ensuring that while this information is open and transparent on the chain, only both parties to the transaction and the necessary validation nodes can decrypt it and view it, thus protecting the privacy of the transaction. The code logic and input data of smart contracts can go through FHE first and then be executed on the chain. Even if the contract code and input data are visible to the public, the calculation results remain encrypted and only the contract participants can decrypt the results, protecting commercial secrets and the privacy of the execution process. In multi-chain or cross-chain environments, FHE ensures that data passed between different blockchains always remains encrypted, and only the authorized nodes of the destination chain can decrypt the data, preventing data leakage in the intermediate links and supporting secure cross-chain data sharing and collaboration. In addition, the encrypted data on the blockchain can be aggregated and statistically operated to generate encrypted analysis results, which facilitates on-chain or off-chain market trend analysis, risk assessment, etc., without exposing individual data. In addition, a decentralized machine learning platform based on homomorphic encryption can be established, where each participating node contributes encrypted training data to jointly train models and protect data privacy.

WiMi基於機器學習和完全同態加密算法的數據加密技術可以在區塊鏈中用於隱私保護交易、私有智能合約、跨鏈數據交換和協作、鏈上數據分析和機器學習等場景。例如,交易雙方都可以使用FHE來加密敏感信息,例如交易金額、資產類型和交易目的,從而確保儘管這些信息在鏈上是公開和透明的,但只有交易雙方和必要的驗證節點才能對其進行解密和查看,從而保護交易的隱私。智能合約的代碼邏輯和輸入數據可以先通過FHE,然後在鏈上執行。即使合約代碼和輸入數據對公衆可見,計算結果仍處於加密狀態,只有合同參與者才能解密結果,從而保護商業祕密和執行過程的隱私。在多鏈或跨鏈環境中,FHE確保在不同區塊鏈之間傳遞的數據始終保持加密狀態,只有目標鏈的授權節點才能解密數據,從而防止中間環節中的數據泄露並支持安全的跨鏈數據共享和協作。此外,可以對區塊鏈上的加密數據進行彙總和統計操作以生成加密的分析結果,這有助於鏈上或鏈下市場趨勢分析、風險評估等,而不會暴露個別數據。此外,可以建立基於同態加密的去中心化機器學習平台,其中每個參與節點都提供加密的訓練數據,以共同訓練模型並保護數據隱私。

Blockchain data encryption technology based on machine learning and a fully homomorphic encryption algorithm integrates cutting-edge encryption and artificial intelligence technologies, aiming to provide strong data protection capabilities for blockchain while maintaining the transparency and decentralized characteristics of blockchain, which provides security for the blockchain ecosystem through intelligent key management, fully homomorphic encryption computation, and a balance between privacy protection and transparency. It meets the needs of increasingly complex application scenarios and promotes the development of blockchain technology towards a more secure, private, and practical way.

基於機器學習和完全同態加密算法的區塊鏈數據加密技術集成了尖端的加密和人工智能技術,旨在爲區塊鏈提供強大的數據保護能力,同時保持區塊鏈的透明度和去中心化特徵,區塊鏈通過智能密鑰管理、完全同態加密計算以及隱私保護與透明之間的平衡爲區塊鏈生態系統提供安全性。它滿足了日益複雜的應用場景的需求,並促進了區塊鏈技術向更安全、更私密和更實用的方式發展。

About WIMI Hologram Cloud

關於 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.

WIMI Hologram Cloud, Inc.(納斯達克股票代碼:WIMI)是一家全息雲綜合技術解決方案提供商,專注於專業領域,包括全息增強現實汽車抬頭顯示軟件、三維全息脈衝激光雷達、頭戴式光場全息設備、全息半導體、全息雲軟件、全息汽車導航等。其服務和全息增強現實技術包括全息增強現實汽車應用、三維全息脈衝激光雷達技術、全息視覺半導體技術、全息軟件開發、全息增強現實廣告技術、全息AR娛樂技術、全息ARSDK支付、交互式全息通信和其他全息增強現實技術。

Safe Harbor Statements

安全港聲明

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.

本新聞稿包含1995年《私人證券訴訟改革法》中的 “前瞻性陳述”。這些前瞻性陳述可以通過諸如 “將”、“期望”、“預期”、“未來”、“打算”、“計劃”、“相信”、“估計” 和類似陳述等術語來識別。非歷史事實的陳述,包括有關公司信念和期望的陳述,均爲前瞻性陳述。除其他外,本新聞稿中的業務展望和管理層的報價以及公司的戰略和運營計劃都包含前瞻性陳述。公司還可以在向美國證券交易委員會(“SEC”)提交的20−F和6−K表格的定期報告、向股東提交的年度報告、新聞稿和其他書面材料以及其高管、董事或僱員向第三方發表的口頭陳述中作出書面或口頭的前瞻性陳述。前瞻性陳述涉及固有的風險和不確定性。有幾個因素可能導致實際業績與任何前瞻性陳述中的業績存在重大差異,包括但不限於以下因素:公司的目標和戰略;公司未來的業務發展、財務狀況和經營業績;增強現實全息行業的預期增長;以及公司對其產品和服務的需求和市場接受度的預期。

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.

有關這些風險和其他風險的更多信息包含在公司20-F表年度報告和當前的6-K表報告以及向美國證券交易委員會提交的其他文件中。本新聞稿中提供的所有信息均截至本新聞稿發佈之日。除非適用法律要求,否則公司不承擔任何更新任何前瞻性陳述的義務。

SOURCE WiMi Hologram Cloud Inc.

來源 WiMi Hologram Cloud Inc.

譯文內容由第三人軟體翻譯。


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