Privacy-Preserving Public Auditing for Secure Cloud Storage

By Cong Wang, Sherman S. M. Chow, Qian Wang, Kui Ren, Wenjing Lou. IEEE transactions on computers, 2013

1,129 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9fe91ae98e6119dd6e0efad3c5b5f2f654bf0107

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

In Information Systems

Citations in the corpus

Context-Aware Verifiable Cloud Computing (2017, neutral): “3 Cloud Computing Auditing Current researches about verifiable cloud computing focus on auditing cloud data storage and data integrity with regard to data management, such as insertion, deletion, and addition [1-3, 38, 41].”
A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability (2011, methodology): “Recently, many works [1], [3], [4], [5], [6], [7], [8], [9], [13], [14], [15] focus on providing three advanced features for remote data integrity checking protocols: data dynamics [5], [6], [8], [14], public verifiability [3], [8], [9], [14], and privacy against verifiers [9], [14].”
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.”
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions (2018, background): “In cloud data outsourcing [67], the fog nodes can help users to compute homomorphic authenticators to reduce the computational overhead on users’ devices.”
Data mining with big data (2016, background): “Common approaches are to employ dimension reduction or feature selection [48] to reduce the data dimensions or to carefully include additional samples to alleviate the data scarcity, such as generic unsupervised learning methods in data mining.”
Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data (2012, neutral): “application purposes (see [19], [20], [21], for example).”
Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage (2014, background): “which go as far as allowing a third-party auditor to check the availability of files on behalf of the data owner without leaking anything about the data [3], or without compromising the data owners anonymity [4].”