Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing

By Qian Wang, Kui Ren, Wenjing Lou. 2010

1,900 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kui Ren
2. Wenjing Lou
3. Ning Cao
4. Ming Li
5. Lili Su
6. Jiaming Xu
7. Yudong Chen
8. Hui Li

In Information Systems

1. Kui Ren
2. Wenjing Lou
3. Ning Cao
4. Ming Li
5. Hui Li
6. Nenghai Yu
7. Zhuo Hao
8. Sheng Zhong

Citations in the corpus

• Distributed Statistical Machine Learning in Adversarial Settings (2017, methodology): “In this framework, distributed machine learning is secured via system architectures, hardware devices, and monitoring [14, 27, 31].”
• Big data privacy: a technological perspective and review (2016, background): “It compares different integrity verification schemes discussed [24, 26].”
• Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage (2017, background): “The “privacy” definition in the previous privacy-preserving public auditing scheme [13] requires that the verifier cannot recover the whole blocks from the responses generated by the cloud server.”
• Oruta: Privacy- Preserving Public Auditing for Shared Data in the Cloud (2018, methodology): “3 Homomorphic Authenticators Homomorphic authenticators (also called homomor- phic verifiable tags) are basic tools to construct data auditing mechanisms [2], [3], [6].”
• A survey on security challenges in cloud computing: issues, threats, and solutions (2020, background): “One of the long-dreamed vision of computing as a utility, where users can remotely store their data, is cloud computing which provides high-quality services from a shared pool of configurable computing resources [21].”
• Privacy in the Internet of Things: threats and challenges (2014, background): “Privacy research in cloud computing has focussed on adequate data protection and prevention of information leaks [59, 60], auditing and provenance [61], and private information processing [62, 63].”
• Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, neutral): “In this approach, energy customers can store their energy consumption data on cloud servers and execute computation and queries using the servers’ computational capabilities [60] .”
• A Survey on the Edge Computing for the Internet of Things (2018, background): “For example, in [119], the authors proposed a privacy-preserving public auditing for protecting data stored in the cloud via involving a third-party auditor (TPA).”
• Dynamic Audit Services for Outsourced Storages in Clouds (2013, background): “Data security tracking is crucial for all organizations that should comply with a wide range of federal regulations including the Sarbanes-Oxley Act, Basel II, HIPAA, and so on.1 Furthermore, compared to the common audit, the audit services for cloud storages should provide clients with a more…”
• Security and privacy for storage and computation in cloud computing (2014, background): “Even though public auditability of secure storage in cloud [33] [34] has been proposed in the context, public auditability in secure computation receives less attentions.”
• State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, background): “On the other hand, for ensuring the privacy even with TPAs (Third Party Auditor), in [72] is proposed a privacy-preserving public auditing system for data storage security which guarantee that TPAs would not learn any knowledge 840”
• Panda: Public Auditing for Shared Data with Efficient User Revocation in the Cloud (2015, methodology): “They have been widely used as building blocks in the previous public auditing mechanisms [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [19], [20].”
• A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “Privacy-preserving techniques have been proposed in many scenarios including cloud [48, 4], smart grid [40], wireless network [39] , and online social network [35].”
• Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud (2013, background): “That is, the cloud server will not maliciously delete or modify user data due to the protection of data auditing schemes [17], [18], but will try to learn the content of the stored data and the identities of cloud users.”