Abstract Nowadays, more and more people are expanding their network through online social network services, such as friend recommendation. To improve user experience, the social network service provider wishes to outsource its services to a powerful cloud. Since the cloud is always untrusted, uploading query data and users’ information to it may cause serious privacy issues. Although some schemes have been proposed to address these privacy concerns, there are still some problems in privacy and efficiency. To deal with these problems, we propose a privacy-preserving friend recommendation scheme that is more secure and efficient than the state-of-the-art work. Specifically, based on three-party secret sharing ( TPSS ) scheme, we propose a secure threshold testing ( STT ) protocol to check whether an encrypted value is greater than the given threshold value. Second, we design a more secure and efficient friend recommendation scheme with the help of our proposed STT and the homomorphic properties of TPSS . Finally, the security of our scheme is proved in the semi-honest model, and the privacy of users is well preserved. Also, we evaluate the performance of the proposed scheme through extensive experiments. The results demonstrate that our proposed scheme outperforms the state-of-the-art.

Vehicle platoon offers numerous benefits in terms of road safety, energy efficiency, and traffic management in urban freight delivery. Privacy preservation is critical here: location information ties to customer confidentiality and reputation guarantees platoon reliability, yet most existing platoon management schemes fail to preserve privacy while achieving vehicle location-matching. Meanwhile, traditional distance calculation methods such as Euclidean distance are unsuitable for urban road layouts, and most schemes assume member vehicles must follow to unified endpoints, a rigid constraint conflicting with the scenario's needs. In this paper, we propose a privacy-preserving platoon management scheme for flexible platoon splitting in urban freight delivery (PPFPS). In detail, the PPFPS scheme leverages location and reputation to achieve flexible platoon splitting in platoon management while preserving vehicle privacy. Specially, we design an encrypted Manhattan distance calculation method (EMC) by combining bloom filters and Paillier cryptosystem, which is tailored to the road layouts in urban environments and deployed on cloud servers. The EMC method enables privacy-preserving location matching to achieve flexible platoon splitting, and reputation is used to ensure the reliability of vehicle platoon. Furthermore, the EMC method significantly minimizes the involvement of the trusted authority by introducing cloud-assisted approaches. Theoretical analysis demonstrates that the PPFPS scheme effectively preserves privacy and defends a variety of potential attacks. Simulation evaluation confirms that the PPFPS scheme supports more functions while significantly reducing computation overheads by 66.59% to 78.72% on the TA side, and maintains communication overheads of the similar order of magnitude as the existing schemes.

With the increasing demand for sensing data, the selection efficiency of task participants in mobile crowdsensing (MCS) becomes more and more important. How to efficiently choose eligible task participants while preserving privacy is an urgent issue. In this paper, we propose an efficient and privacy-preserving participant selection scheme based on location in MCS, named EPPS. Specifically, the k-anonymity technique is adopted to generate anonymous location matrices to preserve location privacy of sensing tasks and task participants. To improve selection efficiency, the anonymous location matrices of multiple sensing tasks are consolidated into a matrix called merged location matrix. Similarly, the anonymous location matrices of multiple task participants are also merged. Then, the cloud server computes the Hadamard product for merged location matrices to judge whether task participants locate in the corresponding sensing areas. By judging the merged location matrices of sensing tasks and task participants, multiple participants which satisfy the requirements of sensing tasks can be selected at the same time. Finally, the performance evaluations demonstrate that the proposed scheme outperforms the existing schemes in terms of efficiency, to be specific, the computation overhead of EPPS is decreased by 77% - 85%, the communication overhead of EPPS is decreased by 41%, the selection efficiency of the EPPS is increased by 66% when multiple task participants and one sensing tasks, and the selection efficiency of the EPPS is increased by 96% when multiple task participants and multiple sensing tasks.

Incentive plays an important role in mobile crowdsensing (MCS), as it impels mobile users to participate in sensing tasks and provide high-quality sensing data. However, considering the privacy (including identity privacy, sensing data privacy, and reputation value privacy) and practicality (including reliability, quality awareness, and efficiency) issues in practice, it is a challenge to design such an effective incentive scheme for MCS applications. Existing studies either fail to provide adequate privacy-preserving capabilities or have low practicality. To address these issues, we propose a scheme called BRRV in MCS which relies on two rounds of range reliability assessment to guarantee the reliability of data while achieving privacy preservation. In addition, we also present a lightweight scheme called LRRV in MCS which relies on a single round of range reliability assessment to guarantee the reliability of data while achieving lightweight and privacy preservation. Moreover, to fairly stimulate participants, constrain participants' malicious behavior, and improve the probability of high-quality data, we design a quality-aware reputation-based reward and penalty strategy to achieve dual incentives (including money incentives and reputation incentives) for participants. Furthermore, comprehensive theoretical analysis and experimental evaluation demonstrate that our proposed schemes are significantly superior to the existing schemes in several aspects.

Federated learning (FL) trains a model over multiple datasets by collecting the local models rather than raw data, which can help facilitate distributed data analysis in many real-world applications. Since the model parameters can leak information about the training datasets, it is necessary to preserve the privacy of the FL participants’ local models. Furthermore, FL is vulnerable to poisoning attacks which can significantly decrease the model utility. To settle the above issues, we propose a privacy-preserving and Byzantine-robust FL scheme$\Pi _{\text{P2Brofl}}$that maintains robustness in the presence of poisoning attacks and preserves the privacy of local models simultaneously. Specifically,$\Pi _{\text{P2Brofl}}$leverages three-party computation (3 PC) to securely achieve a Byzantine-robust aggregation method. To improve the efficiency of privacy-preserving local model selection and aggregation, we propose a maliciously secure top-$k$protocol$\Pi _{\text{top}-k}$that has low communication overhead. Moreover, we present an efficient maliciously secure shuffling protocol$\Pi _{\text{shuffle}}$since secure shuffling is necessary for our secure top-$k$protocol. The security proof of the scheme is given and experiments on real-world datasets are conducted in this paper. When the proportion of Byzantine participants is 50%, the error rate of the model only increases by 1.05% while it increases by 23.78% without using our protection.

With the pervasiveness of GPS-enabled devices, mobile users can directly visit the best travel routes matching their interests and obtain a better user experience via location-based travel recommendation services. As the number of queries grows, the travel agency for location-based travel recommendations tends to outsource its recommendation services to the cloud server. Since the travel agency’s popular travel routes and raw trajectory data from mobile users contain sensitive information, privacy protection should be guaranteed. Although some schemes have been proposed to solve the privacy problems, no previous works related to the location-based recommendation are proposed over mobile users’ raw trajectories. To solve this problem, we propose a privacy-preserving travel recommendation scheme based on stay points over the raw encrypted trajectory data. Specifically, we first propose an adapted longest common subsequence computation algorithm to measure the similarity of two trajectories. Second, to support some computations under ciphertext, we design several secure two-party computation (S2PC) primitives (e.g., secure division, secure mean coordinate, and secure comparison) based on the Paillier cryptosystem. Third, we implement secure stay points extraction and adapted longest common subsequence computation protocols via these secure computation primitives. Finally, we analyze the security of our proposed scheme in the semi-honest model and show that the privacy of mobile users’ trajectories, query results, and the travel agency’s popular travel routes are well protected. Meanwhile, we evaluate the performance of each secure computation primitive and conduct extensive experiments on synthetic datasets, and the experimental results show that our scheme is practical in the real applications.

Platoon refers to a group of vehicles traveling in a train-like strategy with a lean inter-vehicle gap, which can increase road capacity and reduce energy consumption. A platoon is composed of several member vehicles and one leader vehicle which determines the driving pattern of the platoon. Therefore, it is crucial to select a vehicle with the highest reputation value as the leader vehicle in a platoon. Reputation value is a private parameter of each vehicle, and how to preserve its privacy is also an issue worth paying attention to. Therefore, in this paper, a reputation-based and privacy-preserving platoon management (RPPM) scheme in vehicular networks is proposed. Specifically, we design a secure comparison protocol (SCP) to select a leader vehicle for each platoon. The SCP protocol not only reduces the involvement of trust authority but also preserves the privacy of vehicles’ reputation values. Furthermore, the cloud server aggregates reputation ciphertexts and feedback scores of the member vehicles based on the homomorphism characteristic of Paillier ciphertexts, and the reputation value privacy of member vehicles is preserved without affecting the aggregation results. The theoretical analysis indicates that the RPPM scheme is privacy-preserving and secure enough to resist several common attacks in vehicular networks. Simulations are conducted to demonstrate the performance of the RPPM scheme, and the results show that the RPPM scheme significantly outperforms the existing schemes in computation and communication overheads.

Mobile crowdsensing (MCS) is widely applied in vehicular networks where several sensing vehicles complete the same sensing task. Recently, the privacy and reliability of sensing vehicles have aroused extensive attention of researchers in academia. Although the majority of existing schemes have achieved anonymous authentication with large computation and communication overheads, they do not take the reliability of sensing vehicles into account when selecting sensing vehicles. In this article, we propose an efficient anonymous authentication and privacy-preserving reliability evaluation (AARE) scheme for MCS, which not only improves the efficiency of mutual authentication but also guarantees the reliability of sensing vehicles. Specifically, an efficient anonymous authentication method is proposed to achieve the anonymous authentication of sensing vehicles. Besides, even if a sensing vehicle passes anonymous authentication, it is difficult to fully guarantee its reliability. Then, a privacy-preserving reliability evaluation algorithm is adopted to evaluate the reliability of sensing vehicles. Meanwhile, in dynamic vehicular networks, the reliability of sensing vehicles is uncertain since there exist many attacks, thus, an accurate reputation update algorithm is designed. Subsequently, the privacy features, computation, and communication overheads in the proposed scheme are analyzed. Comparisons with the existing schemes show that the authentication efficiency of the proposed scheme is increased by 46%–74%, the communication overhead is reduced by 50%–80%, and the accuracy of reputation values of sensing vehicles which submit the reliable/unreliable sensing data is improved/reduced by 16%–22%/67%–74%.

In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this article proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.

Mobile crowdsensing (MCS) refers to a group of mobile users utilizing their sensing devices to accomplish the same sensing task. However, in vehicular networks, how to evaluate the reliability of sensing vehicles and achieve lightweight privacy preservation are urgent issues. Therefore, this paper proposes a lightweight privacy preservation scheme with efficient reputation management (PPRM) for MCS in vehicular networks. Specifically, we design a lightweight privacy-preserving sensing task matching algorithm which can preserve the location privacy, identity privacy, sensing data privacy, and reputation value privacy while reducing communication and computation overheads of sensing vehicles. In particular, to prevent reputation values from being forged and select reliable sensing vehicles, we present a privacy-preserving reputation value equality verification algorithm to verify reputation values and a privacy-preserving reputation value range proof algorithm to choose sensing vehicles. Afterwards, a three-factor reputation value update algorithm is constructed to efficiently and accurately update the reputation values for sensing vehicles. Simulations are conducted to demonstrate the performance of the PPRM scheme, and the results show that the PPRM scheme significantly outperforms the existing schemes in security and robustness aspects.

As a new sensing paradigm, mobile crowdsensing (MCS) needs to select task participants that satisfy the sensing requirements to accomplish sensing tasks. How to choose eligible task participants while preserving privacy is an urgent issue. In this paper, we propose a lightweight privacy-preserving participant selection (LPPS) scheme for MCS. Specifically, data requesters and task participants utilize the k-anonymity technique to generate anonymous location matrices, then the cloud server computes the Hadamard product for location matrices to judge whether task participants locate in the corresponding sensing areas. Meanwhile, the Paillier algorithm is adopted to ensure sensing data privacy. Finally, both theoretical analysis and performance evaluation demonstrate that the proposed scheme outperforms the existing schemes in terms of security and efficiency.

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, and the adoption of the space–air–ground-integrated network (SAGIN) architecture in vehicular networks can greatly improve the performance of vehicular networks by leveraging the respective advantages of the space, air, and ground segments on coverage, flexibility, reliability, and availability, which results in space–air–ground-integrated vehicular networks (SAGIVNs). Trust management is an important tool for constructing trustworthy SAGIVNs, and privacy preservation is also a primary concern in SAGIVNs. They have conflicting requirements and a satisfactory balance between them is urgently required. In this article, we propose a novel privacy-preserving trust management (PPTM) scheme for the emergency message dissemination in SAGIVNs. The proposed scheme can realize precise trust management and strong conditional privacy preservation simultaneously with low communication overhead, and can provide strong applicability, strong robustness, and multiple other attractive features. Furthermore, the exhaustive theoretical analysis and simulation evaluation are detailed. The results reveal that the proposed scheme is significantly superior to the existing schemes in several aspects.