Since traditional federated learning algorithms cannot provide sufficient privacy guarantees, an increasing number of approaches apply local differential privacy (LDP) techniques to federated learning to provide strict privacy guarantees. However, the privacy budget heavily increases proportionally with the dimension of the parameters, and the large variance generated by the perturbation mechanisms leads to poor performance of the final model. In this paper, we propose a novel privacy-preserving edge federated learning framework based on LDP (PPeFL). Specifically, we present three LDP mechanisms to address the privacy problems in the federated learning process. The proposed filtering and screening with exponential mechanism (FS-EM) filters out the better parameters for global aggregation based on the contribution of weight parameters to the neural network. Thus, we can not only solve the problem of fast growth of privacy budget when applying perturbation mechanism locally, but also greatly reduce the communication costs. In addition, the proposed data perturbation mechanism with stronger privacy (DPM-SP) allows a secondary scrambling of the original data of participants and can provide strong security. Further, a data perturbation mechanism with enhanced-utility (DPM-EU) is proposed in order to reduce the variance introduced by the perturbation. Finally, extensive experiments are performed to illustrate that the PPeFL scheme is practical and efficient, providing stronger privacy protection while ensuring utility.

Cloud computing enables users to remove the necessity of the need of local hardware architecture, which removes the burden of the users from high computation costs. Therefore, it has attracted much attention and research has been conducted heavily on it. To protect users' privacy, data is usually encrypted prior to being sent to the cloud server. New cryptographic primitive such as public-key encryption with equality test (PKEET) has been introduced to make it more practical. In PKEET, users can test whether the underlying messages of two ciphertexts encrypted under different public keys are equal or not without the need to decrypt those ciphertexts. This is a very useful tool, especially for the cloud database, since PKEET mainly focuses on the equality test between two ciphertexts. However, in practice, the cloud server may need to verify the equivalence among more than two ciphertexts. This leads to disclosing unnecessary information of users and redundant computation cost will also occur when using traditional PKEET schemes. How to make this more efficient and practical remains an interesting research problem. In this paper, to solve the aforementioned problems by providing a novel concept of public-key encryption with multi-ciphertext equality test (PKE-MET).

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks.

In a traditional health system, it merely depends on doctors’ initiative reports to discover infectious diseases, which causes late responses from the Center for Disease Control (CDC) and therefore may result in snowballed loss of lives and economy. Sometimes, the disease has spread when doctors realize it is infectious, and the CDC has to invest more human and material resources to control it. In this article, we propose a new secure infectious diseases detection system with the help of the IoT-based e-health platform. In our system model, the hospitals collect patients’ electronic health records (EHRs) and outsource the encrypted EHRs to the contracted cloud. The CDC can regularly send a test query to the cloud server to check whether there are patients who have similar symptoms or some increasing signs, which are regarded as signs of infectious diseases. With this system, the CDC can find the small signs of infectious diseases so that it can make appropriate and timely measures to save more lives. To enable the cloud server to perform the required test, we propose a new cryptographic notion, called public-key encryption with DFET (PKE-DFET), with which we can check whether the underlying messages of two ciphertexts are equal or not after ignoring the bits on designated positions without decryption. The cloud server can utilize the PKE-DFET to flexibly count the number of patients with similar symptoms following the CDC’s instructions. We first instantiate the PKE-DFET into a concrete construction, where anyone can be a tester to perform the DFET on ciphertexts. Finally, we extend our PKE-DFET construction to enable it to be flexible in different actual application scenarios.

Cloud computing offers on-demand availability of computing resources over the Internet. κ-times anonymous authentication (κ-TAA) is an attractive approach to construct pricing schemes, providing access controllability, user anonymity and public traceability. In κ-TAA schemes, authenticated users are permitted to anonymously access services from a provider at most κ times, while the ones whose the number of access times exceeds κ can be publicly traced. That is, κ-TAA schemes offer a prepaid plan that charges users based on the amount of access times. Alternatively, pay-as-you-go (PAYG) is a pricing strategy that allows users to be charged based on the amount of usage, reducing the costs on unnecessary resources. Adopting κ-TAA schemes to PAYG model, the access bound κ is decided by the prepayment amount and the service usage is tracked by the number of access times. However, this approach is impractical, since existing κ-TAA schemes only allow an one-time access in an authentication. This work aims to bridge this gap in the literature by designing an efficient and secure authentication system for PAYG cloud computing, supporting flexible access controllability, user anonymity and public traceability.

Public-key encryption with equality test (PKEET) provides cloud servers with an effective way to check the equality of outsourced encrypted data without decryption. This enables PKEET to attract much attention and be widely researched in cloud computing. However, we claim that the existing PKEET schemes suffer from an inherited problem, called message-consistency unverifiability of testers (MCUT). Applying the MCUT problem, outsourcers can fool cloud servers into outputting incorrect testing results of encrypted data, which negates the practicability of PKEET in cloud computing. We investigate the PKEET literature and find the main reason for the MCUT problem is the independence between the messages inserted in the decryption and testing modules in their ciphertexts. To bridge the technical gap between PKEET and its practical applications, we present a new notion, called PKE with tester verifiable equality test (PKE-TVET), which solves the MCUT problem by allowing testers to verify the message consistency in two modules. We then instantiate the PKE-TVET and give a specific construction in the standard model. In our PKE-TVET scheme, the testing module is integrated into the decryption module so that there is only one message inserted in the ciphertext for both decryption and testing. This special setting lets our scheme directly get rid of the MCUT problem. For better applications in actual scenarios, we further extend the scheme to support authorization and tester designation. Finally, we analyze the tradeoff of parameter sizes and computation costs for the security against MCUT attacks in our PKE-TVET scheme.