Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing

By Qian Wang, Kui Ren, Wenjing Lou, Jin Li. European Symposium on Research in Computer Security, 2009

1,964 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b3d6e545cf259c63b81526624e2a0a47844818aa

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. Wenjing Lou
2. Kui Ren
3. Kristin E. Lauter
4. Boyang Wang
5. Qian Wang
6. Jiale Zhang
7. Hui Li
8. Nenghai Yu

In Information Systems

1. Kui Ren
2. Wenjing Lou
3. Boyang Wang
4. Qian Wang
5. Hui Li
6. Nenghai Yu
7. Zhuo Hao
8. Sheng Zhong

Citations in the corpus

• A survey on security issues in service delivery models of cloud computing (2011, background): “This unique attribute, however, poses many new security challenges (Cong Wang et al., 2009).”
• Enabling Security in Cloud Storage SLAs with CloudProof (2011, background): “This means that B’s chain hash depends on C’s chained hash, which is not possible because the cloud would not know C’s client-provided randomness when he has to answer to B.”
• A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability (2011, methodology): “Protocols in [9], [13] can be adapted to support data dynamics by using the techniques of [8].”
• Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage (2017, background): “Due to its necessity and practicability, remote data integrity checking has attracted extensive research interest [7]–[10], [12]–[15], [17]–[20], [24], [25] in recent years.”
• Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues (2018, methodology): “In edge computing, the research of data integrity should be focused on the following four functional aspects: batch auditing [49], dynamic auditing [50], privacy-preserving [51], and low complexity [52].”
• Entrusting Private Computation and Data to Untrusted Networks (2013, background): “Index Terms—Privacy, trusted computing, untrusted networks, distributed computation, PlanetLab, sTile, tile assembly model Ç”
• Security and privacy for storage and computation in cloud computing (2014, methodology): “[34] first achieved both public verifiability and dynamic data storage operations employing an Third Party Auditor and improving the proof of retrievability model by using classic Merkle Hash Tree [26] construction for BLS [8] based block tag authentication.”
• Big Data and cloud computing: innovation opportunities and challenges (2017, methodology): “Examples of leveraging cloud computing include supporting geodatabase management (Cary et al. 2010), retrieving and indexing spatial data (Wang et al. 2009), running climate models (Evangelinos and Hill 2008), supporting dust storm forecasting (Huang et al. 2013a), optimizing spatial web portals…”
• A Survey of Computation Offloading for Mobile Systems (2012, background): “Many studies have been conducted to protect outsourced data [14, 75, 76].”
• Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “On the other side, cloud storage also lead to data security problems [190] as the requirements of data integrity checking.”
• Cryptographic Cloud Storage (2010, background): “Improved definitions and constructions were given in [30,14,20,3] and dynamic proofs of storage (where the data can be updated) are considered in [4,21, 33 ].”
• Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing (2010, methodology): “[8] propose to combine BLS based homomorphic authenticator with MHT to support both public auditability and fully data dynamics.”
• Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data (2012, neutral): “application purposes (see [19], [20], [21], for example).”
• 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].”