With the help of patients’ health data, e-Healthcare providers can offer reliable data service for better medical treatment. For example, clinical pathway provides an optimal detailed guidance for the clinical treatment. However, the e-Healthcare providers are incompetent with the huge volumes of e-Healthcare data, and a popular and feasible solution is used to outsource the medical data to powerful cloud servers. Because the medical data are very sensitive yet the outsourced servers are not fully trusted, the straightforward execution of clinical pathway query service will inevitably bring huge privacy risks to patients’ data. Apart from the privacy issues, the efficiency issues also need to be taken into consideration such as the communication overhead and computational cost between servers and providers. Considering the above issues, this paper proposes a Privacy-Preserving Optimization of Clinical Pathway Query scheme (PPO-CPQ) to achieve the secure clinical pathway query under e-Healthcare cloud servers without revealing neither the private information of patients such as name, gender, age and physical index, nor the sensitive information of hospitals such as treatment, medication and expense. In our proposed scheme, it first designs secure and privacy-preserving several sub-protocols, such as privacy-preserving comparison, privacy-preserving clinical comparison, privacy-preserving stage selection and privacy-preserving stage update protocol, to ensure the privacy in e-Healthcare system, then it adopts the greedy algorithm with a secure manner to perform the query and the Min-Heap technology to improve the efficiency. Experimental result shows that our scheme is practical and efficient in terms of computational cost and communication overhead.

Nowadays, patients' physiological data and medical records are outsourced to the medical cloud to help the patients and doctors to obtain valuable and reliable services, i.e., diagnosis decision and treatment recommendation, which provides subtle and precise guidance for medical cases. Neighborhood-based recommendation (NBR) provides an effective and vivid approach to finding like-minded nodes to generate recommendations, automating what is usually known as word of mouth. Unfortunately, both diverse medical data and outsource technology raise stringent privacy concerns since the patient data is generally associated with attribute sets which contain somewhat sensitive information, such as age, gender, epidemic, physiological data and clinic result etc. The great privacy-revealing risk will occur if this sensitive medical information is maliciously exploited by network eavesdroppers, untrusted cloud servers or doctors. Considering the above privacy-revealing issues in medical data, this article proposes a privacy-preserving optimization of neighborhood-based recommendation scheme (namely, PPO-NBR), which achieves a secure and privacy-preserving recommendation for medical-aided diagnosis and treatment without revealing the patients' sensitive information. We propose a privacy-preserving medical graph construction protocol and a medical treatment recommendation protocol to implement the secure recommendation on the encrypted medical data, in which it employs BGN Cryptosystem as the building modular to encrypt data and then creatively applies graph theory to expand neighbors to search the best similar features, and uses BLS signature to ensure the authentication, and meanwhile, it deploys a ( t,n) oblivious transfer protocol to preserve the confidentiality of recommendation. A couple of experiments are implemented to evaluate the performance in terms of computational costs and communication overheads and indicate that our scheme is efficient and practical for medical-aided diagnosis.

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key exchange protocol. Secondly, we design a data encoding and aggregating method with data packing which can reduce the computation cost and communication overhead for the system. Thirdly, as the number of participants’ samples are dynamically changeable in MCS, we design a sample number protection method that can implement the security and privacy of the number of training samples owned by participants. Finally, we provide the experimental results on real-world datasets (i.e, MNIST and Car Evaluation) with crowdsensing devices under Raspberry-Pi 4B and Redmi-K30 Pro , respectively, and the results demonstrate that our scheme is more efficient and practical in secure and privacy-enhanced model aggregation for federated learning in mobile crowdsensing.

With the rapid growth of machine learning and artificial intelligence in medical cloud systems, cloud-aided medical computing has materialized a concrete platform for rapid realization of the service-oriented computing paradigm. However, using machine learning approaches in medical clouds might cause serious privacy leakage risk, for example, it needs to take the patients’ sensitive data as the inputs of the machine learning algorithm. In this article, we propose an efficient and privacy-preserving clinical diagnosis scheme that was performed by an (untrusted and malicious) outsourced cloud platform, which can provide the diagnosis assistance for doctors without leaking any sensitive information of patients and service providers. Concretely, we give the security model of privacy-preserving multiclass support vector machine (SVM) in outsourced medical clouds and design a secure clinical diagnosis scheme with privacy-preserving multiclass predecision and diagnosis. We propose a novel encoding approach to implement the encryption of negative number, and design several security building blocks, such as privacy-preserving decision function computing, privacy-preserving classification, and search of max decision function on encrypted fields, to provide the construction of the secure clinical diagnosis with privacy-preserving multiclass SVM. We give the concrete scheme and provide the experiment on Dermatology datasheet. Security analysis and experimental results indicate that the proposed scheme is efficient and practical in privacy-preserving clinical diagnosis systems.

In recent years, many researchers have applied aggregated signature techniques to resource-constrained vehicular Ad Hoc networks (VANET) authentication scenarios. We reviewed two recent VANET authentication schemes based on certificateless aggregated signatures (CLAS) while demonstrating that neither of them is resistant to public key replacement attacks under their security models. An eavesdropper can successfully forge a legitimate signature to perform malicious operation. To further address the above security flaws, we propose an improved CLAS scheme for VANET. Considering that some researchers have briefly or even incorrectly used forking lemmas in their security proofs to prove an insecure CLAS scheme, we further improve and refine the security model and security proof method for CLAS, which make the proof process transparent by using general forking lemma. Based on these improvements, our scheme can resist attacks from two types of adversaries in the CLAS security model. In the final performance analysis, the improved CLAS scheme outperforms the secure related scheme of recent years in terms of both computational and communication efficiency. Therefore, our proposal is more suitable for resource-constrained VANET environments.