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.

The detection of traffic objects in aerial scenes holds significant application potential in both military and civilian sectors. However, current aerial traffic object detection techniques based on computer vision face challenges including limited awareness of object direction, a heavy computational burden on the feature extraction backbone network, and inadequate capacity to learn crucial semantic information. In this paper, our focus is on investigating the mechanisms for predicting the directional perception of traffic objects in aerial scenes, achieving backbone network lightness, and exploring methods for extracting key semantic information from objects. Firstly, to tackle the challenge of poor perception of traffic object direction and angle in aerial scenes, we utilize techniques like equivariant vector field convolution, multi-task anchor-free prediction, and adaptive loss to develop a precise mechanism for recognizing and predicting object directions. Secondly, given the presence of small-sized and numerous objects in aerial scenes, we propose the adoption of a lightweight backbone network employing channel stacking to decrease the model’s computational burden. Additionally, we establish a theoretical framework and methodology for optimizing and compressing this backbone network, aimed at enhancing feature extraction and propagation for aerial traffic objects. Furthermore, to address the issue of inadequate learning of key semantic information features, we incorporate saliency attention and multi-scale contextual information to capture the essential semantic characteristics of the objects. We also establish a method for extracting semantic features specifically for aerial traffic objects. The approach presented in this paper broadens the applicability of aerial object detection algorithms and offers novel methodologies and theoretical foundations for object detection in intricate scenarios.

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.

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.

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.