Aiming at reducing the local storage burden and computational costs, numerous individuals and enterprises are willing to outsource their data to the cloud server. Meanwhile, due to the loss of the actual physical control over their data files once outsourced to the cloud server, how to guarantee the cloud server keep user’s data integrity is an important security issue to be addressed urgently. Accordingly, multiple data integrity checking schemes based on the traditional cryptosystem have been proposed. However, with the advent and development of quantum computer, these existing data integrity checking schemes are no longer secure. Thus, it is necessary to study the new scheme which can resist quantum attack to adapt to the quantum era. In this work, we put forward a novel scheme named lattice-based privacy-preserving and forward-secure cloud storage public auditing scheme (LB-PPFS). Our proposed scheme is not only quantum-attack-against, but also enjoy the privacy-preserving and forward-secure property. In the proposed scheme, a curious auditor cannot learn any knowledge of user’s data because the original data is encapsulated with a random number. In addition, the lattice basis delegation technique is adopted to achieve forward security for resisting key exposure attack. Based on the hardness assumptions of SIS problem from lattice, we prove that the proposed scheme can achieve formally provable security. Besides, the theoretical analysis and performance evaluation demonstrate that the proposed scheme is effective and feasible to guarantee the quantum security for the data integrity in cloud storage.

With the explosion of various mobile devices and the tremendous advancement in cloud computing technology, mobile devices have been seamlessly integrated with the premium powerful cloud computing known as an innovation paradigm named Mobile Cloud Computing (MCC) to facilitate the mobile users in storing, computing and sharing their data with others. Meanwhile, Attribute Based Encryption (ABE) has been envisioned as one of the most promising cryptographic primitives for providing secure and flexible fine-grained “one to many” access control, particularly in large scale distributed system with unknown participators. However, most existing ABE schemes are not suitable for MCC because they involve expensive pairing operations which pose a formidable challenge for resource-constrained mobile devices, thus greatly delaying the widespread popularity of MCC. To this end, in this paper, we propose a secure and lightweight fine-grained data sharing scheme (SLFG-DSS) for a mobile cloud computing scenario to outsource the majority of time-consuming operations from the resource-constrained mobile devices to the resource-rich cloud servers. Different from the current schemes, our novel scheme can enjoy the following promising merits simultaneously: (1) Supporting verifiable outsourced decryption, i.e., the mobile user can ensure the validity of the transformed ciphertext returned from the cloud server; (2) resisting decryption key exposure, i.e., our proposed scheme can outsource decryption for intensive computing tasks during the decryption phase without revealing the user’s data or decryption key; (3) achieving a CCA security level; thus, our novel scheme can be applied to the scenarios with higher security level requirement. The concrete security proof and performance analysis illustrate that our novel scheme is proven secure and suitable for the mobile cloud computing environment.

In this letter, we discuss the security weakness of Wang et al. ’s attribute-based data sharing scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (TIFS) (DOI: 10.1109/TIFS.2016.2549004). Through designing two concrete attacks, we identify two serious security flaws in their scheme. 1) First, we show that their scheme is insecure because in their scheme any authenticated user can freely tamper with the weight of his own attribute to gain higher level decryption privilege to arbitrarily decrypt the ciphertext belonging to another user with higher weight of attribute. 2) Second, we further demonstrate that their scheme is trivial insecure because in their scheme even any malicious authenticated user’s attribute does not match the access policy of a ciphertext, he/she still has the power to decrypt the ciphertext, i.e., the decryption power is independent of attributes, thus, their scheme is not a rigorous attribute-based scheme. The two weaknesses discovered may hinder their scheme infeasible for practical deployment. Accordingly, we present a remedy solution to the issues while preserving all the security features of the original scheme. We hope that our cryptoanalysis and remedy scheme may contribute to avoiding similar design flaws in future designs.

Associative Classification (AC) is envisioned as one of the most attractive classification approaches to facilitate managers for prediction and decision making in a highly accurate and easily interpretable way. The most of existing AC algorithms mainly focus on the two static metrics of association rules: support and confidence. However, in this paper, we identify a potential limitation of the confidence and further point out these AC algorithms merely consider the explicit expression of classification information and knowledge of Class Association Rules (CARs) and neglect the implicit expression of classification information and knowledge of CARs. And even the explicit expression of classification information and knowledge of CARs is equivalent, the implicit expression of classification information and knowledge of them may be different. Thus, these CARs will cause different influence on prediction or decision-making in terms of the interpretability and rationality of classification, and will result in predictive bias or decision-making bias in practice. In response to this drawback, we introduce the notion of information entropy and propose an innovative approach for associative classification based on information entropy of frequent attribute set, named EAC. Different from the existing schemes, the proposed EAC algorithm enjoys the following promising merits: (1) lower predictive bias or decision-making bias by levering the information entropy of frequent attribute set; (2) better interpretability and rationality by setting higher support threshold; (3) setting global optimum parameter dynamically through repeated trials with reducing the dimension of data sets. Experiments on 20 well-known benchmark data sets demonstrate that our EAC approach is highly competitive to other state-of-the-art AC algorithms in terms of predictive bias or decision-making bias, interpretability, and efficiency, which can be used to construct a new classifier efficiently and effectively in many realistic scenarios.