Healthcare Internet of Things (IoT) is an emerging paradigm, which can provide comprehensive and different types of health services and enable various types of medical sensors to monitor patient's health conditions. In the healthcare IoT, patient is deployed with a variety of medical sensors, which continuously monitors and collects patient's sensitive health data that needs specially protection for preventing privacy leakage. To safely send multiple different health data monitored by multiple different medical sensors to multiple corresponding healthcare professionals in one data report, several multi-message and multi-receiver signcryption schemes have been introduced by employing the traditional public key cryptography, identity-based cryptography or certificateless cryptography. However, these schemes suffer from the certificate management, key escrow and key distribution problem. Besides, due to the resource-constraint property of medical sensors, they are unsuitable for healthcare IoT in terms of both performance and privacy requirements. To solve these issues, this paper introduces an efficient anonymous certificate-based multimessage and multi-receiver signcryption scheme for healthcare IoT, where the certificate-based cryptography and elliptic curve cryptography are combined to simplify the certificate management problem, eliminate the key escrow problem, solve the key distribution problem and ensure the privacy-preserving. Furthermore, the security analysis suggests that the proposed scheme is able to achieve the confidentiality, unforgeability, receiver anonymity, sender anonymity and decryption fairness; the performance evaluation indicates that the proposed scheme brings to the lower computation cost and communication cost in comparison to the existing schemes.INDEX TERMS Certificate-based cryptography, elliptic curve cryptography, multi-message and multi-receiver signcryption, healthcare Internet of Things.

No abstract

Multiparty threshold private set intersection (MP-TPSI) protocol allows n mutually untrusted parties P 1 , P 2 , … , P n holding data sets A 1 , A 2 , … , A n of size m respectively to jointly compute the intersection I = A 1 ∩ A 2 ∩ ⋯ ∩ A n over all their private data sets only if the size of intersection is larger than m − t , while ensuring that no other private information of the data sets other than the intersection is revealed, where t is the threshold. In the MP-TPSI protocol, multiple parties first decide whether the size of the intersection is larger than the threshold t ; then, they compute the intersection if the size of the intersection is larger than the threshold t . However, the existing MP-TPSI protocols use different forms of evaluation polynomials in the cardinality testing and intersection computing phases, so that parties need to transmit and calculate a large number of evaluation values, which leads to high communication and computational complexity. In addition, the existing MP-TPSI protocols cannot guarantee the security and the correctness of the results, that is, an adversary can know the additional information beyond the intersection, and the elements that are not in the intersection are calculated as the intersection. To solve these issues, based on the threshold fully homomorphic encryption (TFHE) and sparse polynomial interpolation, we propose an MP-TPSI protocol. In the star network topology, the theoretical communication complexity of the proposed MP-TPSI protocol depends on the threshold t and the number of parties n , not on the size of set m . Moreover, the proposed MP-TPSI protocol outperforms other related MP-TPSI protocols in terms of computational and communication overheads. Furthermore, the proposed MP-TPSI protocol tolerates up to n − 1 corrupted parties in the semi-honest model, where no set of colluding parties can learn the input of an honest party in the strictest dishonest majority setting.

Vehicular sensor networks (VSNs) have emerged as a paradigm for improving traffic safety in urban cities. However, there are still several issues with VSNs. Vehicles equipped with sensing devices usually upload large amounts of data reports to a remote cloud center for processing and analyzing, causing heavy computation and communication costs. Additionally, to choose an optimal route, it is required for vehicles to query the remote cloud center to obtain road conditions of the potential moving route, leading to an increased communication delay and leakage of location privacy. To solve these problems, this paper proposes an efficient privacy-preserving data sharing (EP 2 DS) scheme for fog-assisted vehicular sensor networks. Specifically, the proposed scheme utilizes fog computing to provide local data sharing with low latency; furthermore, it exploits a super-increasing sequence to format the sensing data of different road segments into one report, thus saving on the resources of communication and computation. In addition, using the modified oblivious transfer technology, the proposed scheme can query the road conditions of the potential moving route without disclosing the query location. Finally, an analysis of security suggests that the proposed scheme can satisfy all the requirements for security and privacy, with the evaluation results indicating that the proposed scheme leads to low costs in computation and communication.

Logistic regression is a data statistical technique, which is used to predict the probability that an event occurs. For some scenarios where the storage capabilities and computing resources of the data owner are limited, the data owner wants to train the logistic regression model on the cloud service provider, while the high sensitivity of training data requires effective privacy protection methods that enable efficient model training without exposing information about the training data to untrusted cloud service providers. Recently, several works have used cryptographic techniques to implement privacy-preserving logistic regression in such application scenarios. However, on large-scale training datasets, the existing works still have the problems of long model training time and poor model performance. To solve these problems, based on the homomorphic encryption (HE), we propose an efficient privacy-preserving outsourced logistic regression (P2OLR) on encrypted training data, which enables data owners to utilize the powerful storage and computing resources of cloud service providers for logistic regression analysis without exposing data privacy. Furthermore, the proposed scheme can pack multiple messages into one ciphertext and perform the same arithmetic evaluations on multiple plaintext slots by using the batching technique and single instruction multiple data (SIMD) mechanism in HE. On three public training datasets, the experimental results show that, compared with the existing schemes, the proposed scheme has better performance in terms of the encryption and decryption time of the data owner, the storage of encrypted training data, and the training time and accuracy of the model.