The advances of cloud computing, fog computing and Internet of Things (IoT) make industries more prosperous than ever. However, in this sophisticated system security and privacy issues are major concerns. In this paper, we focus on the issue of assured data deletion. We firstly propose a framework to integrate the cloud, the fog and the things together to manage stored data from industries or individuals. We then focus on secure data deletion in this framework by proposing an assured data deletion scheme which fulfills verifiable data deletion as well as flexible access control over sensitive data. Only data owners and fog devices are involved when deleting cloud data and validating the deletion of these data, which makes the protocol practical. The proposed protocol takes advantage of attribute-based encryption, whose security can be proved under standard model. The theoretical analysis and the implementation results demonstrate the feasibility of our proposal.

Secure cross-domain authorization and authentication (AA) enable application service providers (ASPs) to allow users for resource access from different trusted domains. In this article, we propose a unified blockchain-assisted secure cross-domain AA framework for smart city, which can guarantee transparent cross-domain resource access while preserving user privacy. In the framework, ASPs can flexibly delegate their authentication capabilities to the blockchain, and users authorized by different ASPs can be authenticated by the blockchain where the authentication events are publicly audited and traced. Since the blockchain is publicly accessible, users’ sensitive identity attributes may be exposed during the authentication process. To address privacy leakage caused by the authentication events, several privacy-preserving techniques, including threshold-based homomorphic encryption, zero-knowledge proof, and random permutation, are exploited to hide users’ sensitive information on the blockchain. Moreover, to improve user revocation efficiency, we integrate a cryptographic accumulator and secure hash functions into the framework where ASPs are allowed to revoke their users through a global revocation contract. Our security analysis shows that the proposed framework can achieve all desirable security and privacy properties, and a proof-of-concept prototype has been developed to demonstrate the correctness and efficiency of the proposed framework.

Next-generation wireless networks (NGWN) aim to support diversified smart applications that require frequent data exchanges and collaborative data processing among multiple stakeholders. Data management (DM), including data collection, storage, sharing, and computation, plays an essential role in empowering NGWN. However, DM for NGWN faces two significant challenges: stakeholders' data cannot be easily managed across different trust domains under a distributed network architecture; and privacy preservation requirements of personal data become more rigorous under new privacy regulations. To explore possible solutions to address the challenges, we first investigate the state-of-the-art architecture designs for DM and emphasize advantages of a blockchain-based DM architecture. Then we summarize existing privacy-preserving techniques in terms of advantages and challenges when being applied to DM. In addition, we review recent privacy regulations with their impacts on DM and discuss the existing solutions with privacy regulation compliance based on blockchain. Finally, we identify further research directions for achieving DM with privacy preservation.

In this article, we propose a blockchain-based fair and privacy-preserving data trading scheme that supports fine-grained data selling. First, to achieve fairness for trading participants, by incorporating attribute-based credentials, encryption, and zero-knowledge proof, we design a data trading scheme where a buyer first publishes the required data attributes on the blockchain, and a data seller can demonstrate data availability in ciphertext by only disclosing the required attributes to a data buyer and proving the authenticity of data. A data buyer transfers funds only if the correct key material is uploaded to the blockchain. Second, to guarantee fine-grained data trading and preserve identity privacy, we build a Merkle hash tree on the ciphertexts of data with a signature on its root node, which allows a data seller to split data into blocks and remove the sensitive information from the data without affecting data availability verification. The public key of the data seller is not leaked to the data buyer during the trading. Moreover, different trading transactions from the same data seller cannot be linked. We formally prove that our scheme achieves the desired security properties: fairness and privacy preservation. Simulation results demonstrate the feasibility and efficiency of the proposed scheme.

With the proliferation of cloud computing, data owners can outsource the spatial data from the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage resources and location-based services. However, the outsourced services may raise privacy concerns, since the cloud server may not be fully trusted for both data owners and search users. If the data owners and search users conventionally encrypt the spatial data and query requests, the efficiency and functionality of query processing are weakened. Most of the existing works only focus on spatial data search or keyword search and do not consider spatial keyword search over encrypted data. In this article, we first design a geometric range query (GRQ) scheme, which can generate an arbitrary geometric range to fit the search user’s desired spatial data while protecting location privacy. Furthermore, based on GRQ, we propose a multidimensional spatial keyword similarity search scheme with access control (MSSAC) by integrating the polynomial function and matrix transformation. Specifically, an access control strategy is defined by a role-based polynomial function, which is embedded in the vectors of indices and trapdoors to achieve efficient and lightweight access control. Moreover, MSSAC enables the cloud server to execute compute-then-compare operations for spatial keyword search in a privacy-preserving manner by leveraging techniques of randomizable permutation and matrix multiplication. The formal security analyses and extensive experiments demonstrate that GRQ and MSSAC preserve the privacy of data owners and search users while achieving efficient spatial keyword search.

Decentralized anonymous payment (DAP) enables users to directly transfer cryptocurrencies privately without passing through a central authority. Anonymous cryptocurrencies have been proposed to improve the privacy degree of DAP systems, such as Zerocash and Monero. However, the strong degree of privacy may cause new regulatory concerns, i.e., the anonymity of transactions can be used for illegal activities, such as money laundering. In this paper, we propose a novel DAP scheme that supports regulatory compliance and enforcement. We first introduce regulators into the system, who define regulatory policies for anonymous payment, and the policies are enforced through commitments and non-interactive zero-knowledge proofs for compostable statements. By doing so, users can prove that transactions are valid and comply with regulations. A tracing mechanism is embedded in the scheme to allow regulators to recover the real identities of users when suspicious transactions are detected. The formal security model and proof are provided to demonstrate that the proposed scheme can achieve desired security properties, and the performance evaluation shows its high efficiency.