The paper proposes a secure cloud storage system that supports privacy-preserving public auditing, enabling third-party auditors (TPAs) to verify the integrity of outsourced data without learning the data content. The system ensures that the TPA cannot derive any information about the data during the auditing process, addressing the privacy concerns of users. The proposed scheme is based on homomorphic linear authenticators (HLAs) and random masking techniques, which together prevent the TPA from reconstructing the original data from the server's response. The paper also introduces batch auditing, allowing the TPA to efficiently handle multiple auditing tasks simultaneously, and discusses the extension to support dynamic data operations. Security and performance analyses are provided, demonstrating that the proposed scheme is both secure and efficient.The paper proposes a secure cloud storage system that supports privacy-preserving public auditing, enabling third-party auditors (TPAs) to verify the integrity of outsourced data without learning the data content. The system ensures that the TPA cannot derive any information about the data during the auditing process, addressing the privacy concerns of users. The proposed scheme is based on homomorphic linear authenticators (HLAs) and random masking techniques, which together prevent the TPA from reconstructing the original data from the server's response. The paper also introduces batch auditing, allowing the TPA to efficiently handle multiple auditing tasks simultaneously, and discusses the extension to support dynamic data operations. Security and performance analyses are provided, demonstrating that the proposed scheme is both secure and efficient.