SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

Security Analysis on a Remote Data Auditing Scheme for Distributed Storage Systems

Remote data auditing becomes critical to ensure the storage reliability in distributed cloud storage.Recently,Le et al proposed an efficient private data auditing scheme NC-Audit designed for regenerating codes,which claimed that NC-Audit can effectively realize privacy-preserving data auditing for distributed storage systems.However,our analysis shows that NC-Audit is not secure for that the adversarial cloud can forge some illegal blocks to cheat the auditor successfully with a high probability even without storing the user’s whole data,when the coding field is large enough.

Extending Auditing Models to Correspond with Clients’ Needs in Cloud Environments

The user control over the life cycle of data is of an extreme importance in clouds in order to determine whether the service provider adheres to the client’s pre-specified needs in the contract between them or not, significant clients concerns raise on some aspects like social, location and the laws to which the data are subject to. The problem is even magnified more with the lack of transparency by Cloud Service Providers (CSPs). Auditing and compliance enforcement introduce different set of challenges in cloud computing that are not yet resolved. In this paper, a conducted questionnaire showed that the data owners have real concerns about not just the secrecy and integrity of their data in cloud environment, but also for spatial, temporal, and legal issues related to their data especially for sensitive or personal data. The questionnaire results show the importance for the data owners to address mainly three major issues: Their ability to continue the work, the secrecy and integrity of their data, and the spatial, legal, temporal constraints related to their data. Although a good volume of work was dedicated for auditing in the literature, only little work was dedicated to the fulfillment of the contractual obligations of the CSPs. The paper contributes to knowledge by proposing an extension to the auditing models to include the fulfillment of contractual obligations aspects beside the important aspects of secrecy and integrity of client’s data.

Research on Audit Data Analysis under the Background of Big Data

With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control the operation status and risk assessment of the whole enterprise,timely analyze,control and respond to risks,and protect the enterprise to reduce risks.With the advent of the era of big data,audit data analysis is becoming more and more important.At the same time,a large amount of data analysis also brings challenges to auditors.Methods to deal and solve the challenges has become an urgent problem to be solved at present.This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data,so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.

Public Auditing for Encrypted Data with Client-Side Deduplication in Cloud Storage

Storage auditing and client-side deduplication techniques have been proposed to assure data integrity and improve storage efficiency, respectively. Recently, a few schemes start to consider these two different aspects together. However, these schemes either only support plaintext data file or have been proved insecure. In this paper, we propose a public auditing scheme for cloud storage systems, in which deduplication of encrypted data and data integrity checking can be achieved within the same framework. The cloud server can correctly check the ownership for new owners and the auditor can correctly check the integrity of deduplicated data. Our scheme supports deduplication of encrypted data by using the method of proxy re-encryption and also achieves deduplication of data tags by aggregating the tags from different owners. The analysis and experiment results show that our scheme is provably secure and efficient.

Lightweight and Manageable Digital Evidence Preservation System on Bitcoin

An effective and secure system used for evidence preservation is essential to possess the properties of anti- loss, anti-forgery, anti-tamper and perfect verifiability. Traditional architecture which relies on centralized cloud storage is depressingly beset by the security problems such as incomplete confidence and unreliable regulation. Moreover, an expensive, inefficient and incompatible design impedes the effort of evidence preservation. In contrast, the decentralized blockchain network is qualified as a perfect replacement for its secure anonymity, irrevocable commitment, and transparent traceability. Combining with subliminal channels in blockchain, we have weaved the transaction network with newly designed evidence audit network. In this paper, we have presented and implemented a lightweight digital evidence-preservation architecture which possesses the features of privacy-anonymity, audit-transparency, function-scalability and operation-lightweight. The anonymity is naturally formed from the cryptographic design, since the cipher evidence under encrypted cryptosystem and hash-based functions leakages nothing to the public. Covert channels are efficiently excavated to optimize the cost, connectivity and security of the framework, transforming the great computation power of Bitcoin network to the value of credit. The transparency used for audit, which relates to the proof of existence, comes from instant timestamps and irreversible hash functions in mature blockchain network. The scalability is represented by the evidence chain interacted with the original blockchain, and the extended chains on top of mainchain will cover the most of auditors in different institutions. And the lightweight, which is equal to low-cost, is derived from our fine-grained hierarchical services. At last, analyses of efficiency, security, and availability have shown the complete accomplishment of our system.