Fog-assisted mobile crowdsensing (FA-MCS) alleviates challenges with respect to computation, communication, and storage from the traditional model of mobile crowdsensing (MCS) “requester-server-users.” Data aggregation, as a specific MCS task, has attracted a lot of attentions in mining the potential value of the massive crowdsensing data. However, the process of data aggregation in FA-MCS may threaten the privacies of both users' data and aggregation results. The untrusted server and fog nodes (FNs) may damage the correctness of aggregation results. Moreover, bad FNs, which do not upload data to server or fail to verify successfully, can endanger the reliability of FA-MCS and the accuracy of aggregation results. To tackle these problems, we propose a verifiable, reliable, and privacy-preserving data aggregation scheme for FA-MCS. Specifically, the proposed scheme preserves privacies of both users' data and aggregation results, enables requester to verify the correctness of aggregation result, and is able to tolerate several bad FNs without affecting the data aggregation result. Through formal security analysis, the proposed scheme is shown to be secure and privacy preserving. Extensive experiments also show the proposed scheme is efficient and reliable.

The widespread adoption of cloud computing and the exponential growth of data highlight the need for secure data sharing and querying. Attribute-based keyword search (ABKS) has emerged as an efficient means of searching encrypted data stored in the cloud. However, existing ABKS schemes incur high end-to-end delay and are vulnerable to quantum computer attacks and/or (insider) keyword guessing attacks (KGA). To address these vulnerabilities, this paper introduces a new concept called attribute-based authenticated encryption with keyword search (ABAEKS) and proposes an efficient ABAEKS scheme. Our ABAEKS has low end-to-end delay, and is resistant to both quantum computer attacks and (insider) KGA. In addition, we formalize the security model of ABAEKS system and prove its security in the random oracle model. Finally, we conduct a comprehensive performance evaluation of ABAEKS, and the experimental results show that our ABAEKS is computationally efficient and outperforms current state-of-the-art ABKS schemes.

Fine-grained access control systems facilitate granting differential access rights to a set of users and allow flexibility in specifying the access rights of individual users. As an important fine-grained access control technique, key-policy attribute-based encryption (KP-ABE) has been introduced to achieve fine-grained access control over encrypted data, where each ciphertext is associated with an attribute set such that users satisfying the attribute set can decrypt the ciphertext. In the real-world application scenarios of KP-ABE, various situations such as users leaving the system, compromise of users’ private keys, and business requirements frequently occur, necessitating the revocation of decryption rights for large-scale users. To address the user revocation, numerous revocable KP-ABE schemes have been proposed. However, existing revocable KP-ABE schemes are vulnerable to quantum computer attacks. More importantly, existing solutions fail to address the user addition, where users capable of decrypting certain ciphertexts would like to grant decryption rights to others; this is a highly common requirement, such as changes in user decryption permissions and business needs. This paper explores a potentially new avenue of research to address the above issues by introducing a novel cryptographic primitive called key-policy ABE with switchable attributes (KP-ABE-SA). In the KP-ABE-SA system, each ciphertext linked to an attribute set can be transformed into one associated with another (distinct) attribute set, enabling both user revocation and addition. Furthermore, to withstand quantum computer attacks, we construct a KP-ABE-SA scheme based on the Learning with Errors (LWE) assumption, which is widely believed to be quantum-resistant. Finally, we conduct a comprehensive performance evaluation of our LWE-based KP-ABE-SA scheme, and the experimental results show that the proposed LWE-based KP-ABE-SA scheme is efficient and practical.

Proxy re-encryption (PRE), as a promising cryptographic primitive for secure data sharing in clouds, has been widely studied for decades. PRE allows the proxies to use the re-encryption keys to convert ciphertexts computed under the delegator’s public key into ones that can be decrypted using the delegatees’ secret keys, without knowing anything about the underlying plaintext. This delegable property of decryption rights enables flexible cloud data sharing, but it raises an important issue: if some proxies reveal their re-encryption keys, or collude with some delegatees to create a pirate decoder, then anyone who gains access to the pirate decoder can decrypt all ciphertexts computed under the delegator’s public key without the delegator’s permission. This paper opens up a potentially new avenue of research to address the above (re-encryption) key abuse problem by proposing the first public trace-and-revoke PRE system, where the malicious delegatees and proxies involved in the generation of a pirate decoder can be identified by anyone who gains access to the pirate decoder, and their decryption capabilities can subsequently be revoked by the content distributor. Our construction is multi-hop, supports user revocation and public (black-box) traceability, and achieves significant efficiency advantages over previous constructions. Technically, our construction is a generic transformation from inner-product functional PRE (IPFPRE) that we introduce to trace-and-revoke PRE. In addition, we instantiate our generic construction of trace-and-revoke PRE from the Learning with Errors (LWE) assumption, which was widely believed to be quantum-resistant. This is achieved by proposing the first LWE-based IPFPRE scheme, which may be of independent interest. Finally, we conduct a comprehensive performance evaluation of our LWE-based trace-and-revoke PRE scheme, and the experimental results show that the proposed LWE-based trace-and-revoke PRE scheme is practical and outperforms current state-of-the-art traceable PRE schemes.

Privacy-preserving data aggregation in mobile crowdsensing (MCS) focuses on mining information from massive sensing data while protecting users' privacy. The existence of multiple concurrent tasks is common in urban environments, so privacy-preserving multi-task data aggregation is essential and useful to a large-scale crowdsensing server. However, existing privacy-preserving data aggregation schemes in MCS mainly focus on the single-task data aggregation and the privacy protection of user's data. Little attention is paid to the privacy of user's decision of accepting tasks. Therefore, we propose a privacy-preserving and server-oriented efficient multi-task data aggregation scheme for MCS based fog computing. The proposed scheme can aggregate multiple concurrent tasks from multiple requesters (e.g., for 9 tasks, the proposed scheme completes all tasks in one round as opposed to existing schemes, which finish 9 tasks in nine rounds). Our scheme protects the privacy of user's decision, user's data, and aggregation result of each requester under collusion attacks. Through formal security analyses, our scheme is proved to be secure and privacy-preserving. Both theoretical analyses and experiments show our scheme is efficient.

In mobile crowdsensing (MCS), providing appropriate rewards is a common and efficient way to motivate participants to participate in sensing tasks. However, the privacy of task participants is not protected well in most quality-aware incentive schemes. Moreover, these schemes are designed for general MCS application scenarios where data are collected by internal sensors embedded in participants’ smartphones, and not suitable for scenarios where additional sensors except internal sensors are to collect data (e.g. household medical devices). In scenarios with additional sensors, malicious participants can fabricate sensing data instead of collecting data from additional sensors, i.e., the source reliability of sensing data cannot be ensured. To address these issues, we propose PSIM, a privacy-preserving and source-reliable incentive mechanism scheme for MCS with additional sensors. We combine redactable signature with private hash function to achieve the source reliability verification of sensing data without revealing the privacy of participants. Moreover, rewards are divided into two parts: fixed rewards and floating rewards, to enhance the flexibility of rewards distribution. Both formal theoretical analysis and extensive experimental evaluations on a real dataset show that the proposed PSIM is secure and efficient.