To ensure the privacy, integrity, and security of the user data and to prevent the unauthorized access of data by illegal users in the blockchain storage system is significant. Blockchain networks are widely used for the authentication of data between the data user and the data owner. However, blockchain networks are vulnerable to potential privacy risks and security issues concerned with the data transfer and the logging of data transactions. To overcome these challenges and enhance the security associated with blockchain storage systems, this research develops a highly authenticated secure blockchain storage system utilizing a rider search optimized deep Convolution Neural Network(CNN) model. The architecture integrates the Ethereum blockchain, Interplanetary File System (IPFS), data users, and owners, in which the Smart contracts eliminate intermediaries, bolstering user-owner interactions. In tandem, blockchain ensures immutable transaction records, and merging IPFS with blockchain enables off-chain, distributed storage of data, with hash records on the blockchain. The research accomplishes privacy preservation through six-phase network development: system establishment, registration, encryption, token generation, testing, and decryption. Parameters for secure transactions are initialized, user registration provides genuine user transaction credentials, and encryption guarantees data security, employing optimized Elliptic Curve Cryptography (ECC). Further, the optimized ECC algorithm is developed utilizing a novel rider search optimization that utilizes search and rescue characteristics of human, and rider characteristics for determining the shorter key lengths. Token generation involves issuing digital tokens on a blockchain platform, followed by testing using a deep CNN classifier to detect anomalies and prevent unauthorized data access during the test phase. The decryption of data is conducted for registered users. The developed rider search optimized deep CNN model attains 96.68% accuracy, 96.68% sensitivity, 96.68% specificity for models and ECC encryption with rider search optimization attains 0.0117 ms Decryption time, 4.583 ms Encryption time, 83.28% Genuine User Detection rate(GUD), 364.80 kbs Memory usage, 0.843 s responsiveness for 50 users, which is more efficient.

保证用户数据的隐私性、完整性和安全性，防止非法用户对区块链存储系统中的数据进行非授权访问具有重要意义。区块链网络广泛用于数据用户和数据所有者之间的数据认证。然而，区块链网络容易受到与数据传输和数据交易记录相关的潜在隐私风险和安全问题的影响。为了克服这些挑战并增强与区块链存储系统相关的安全性，本研究利用骑手搜索优化的深度卷积神经网络（CNN）模型开发了一种经过高度验证的安全区块链存储系统。该架构集成了以太坊区块链、星际文件系统（IPFS）、数据用户和所有者，其中智能合约消除了中介机构，增强了用户与所有者的交互。同时，区块链确保了交易记录的不可变性，并且将 IPFS 与区块链合并可以实现链下分布式数据存储，并在区块链上存储哈希记录。研究通过系统建立、注册、加密、令牌生成、测试和解密六个阶段的网络开发来实现隐私保护。初始化安全交易参数，用户注册提供真实的用户交易凭证，加密采用优化的椭圆曲线密码术（ECC）保证数据安全。此外，优化的 ECC 算法是利用新颖的骑手搜索优化开发的，该优化利用人类的搜索和救援特征以及骑手特征来确定较短的密钥长度。代币生成涉及在区块链平台上发行数字代币，然后使用深度 CNN 分类器进行测试，以检测异常并在测试阶段防止未经授权的数据访问。数据解密是针对注册用户进行的。开发的骑手搜索优化深度 CNN 模型的准确度为 96.68%，灵敏度为 96.68%，模型特异性为 96.68%，并且骑手搜索优化的 ECC 加密达到 0.0117 毫秒解密时间，4.583 毫秒加密时间，83.28% 真实用户检测率 (GUD)， 364.80 kbs 内存占用，50 个用户响应时间 0.843 s，效率更高。