Time series data analysis, employing dynamic time warping (DTW) algorithms, has a wide range of applications in fields such as medicine and economics. Given the widespread distribution of data across different domains, integrating and analyzing these datasets through outsourced cloud computing can enhance analytics, though privacy concerns arise. Privacy preserving data analysis, underpinned by secure multi-party computing, emerges as a crucial approach to address this challenge. However, existing efforts face high communication costs and increased interactions, resulting in significant efficiency constraints in practical applications. In this paper, we propose a function secret sharing (FSS)-based framework for secure collaborative analysis of time series data using the DTW algorithm. Utilizing the distributed comparison function, we develop efficient building blocks with minimal online interaction and communication, enhancing the practicability of security protocols. To address the challenges of FSS key generation due to uncertain computational topology when cascading multiple distances, we adopt a modular design and decompose the analysis process into several critical modules. Furthermore, our framework efficiently supports various constraint methods for DTW. We implement and evaluate our framework using publicly available datasets. The results demonstrate a significant reduction in communication costs and the number of interactions during the online phase.

Anomaly detection plays a vital role in processing multi-source data through public cloud servers, yet existing privacy-preserving schemes fail to efficiently detect anomalies while protecting data source privacy. Although isolation forest offer advantages for unsupervised high-dimensional data analysis, implementing its tree-based privacy-preserving mechanisms remains challenging. In this paper, we propose IFAD, a novel isolation forest-based scheme for detecting anomalies in private data. IFAD guarantees end-to-end privacy protection by safeguarding original data, tree structures, and intermediate information throughout detection workflows. Our design achieves efficiency through three key contributions: 1) Cryptographic building blocks combining function secret sharing (FSS) and secret sharing (SS) to enable secure computations; 2) A split index protocol and layer update protocol to facilitate efficient, layer-by-layer isolation forest construction; 3) A detection phase optimization converting the anomaly score calculations into lookup table operations. Experimental evaluations demonstrate that IFAD achieves superior performance, outperforming prior schemes by 2.4×-3.1× in runtime under LAN and WAN environments, and by 1.8×-7.8× in online communication overhead, while maintaining comparable detection accuracy. Our solution establishes an effective balance between privacy preservation and operational efficiency for cloud-based anomaly detection.

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in various data analysis tasks. Recently, PCA has gained widespread use in cloud outsourcing services due to its effectiveness and versatility. However, privacy concerns in outsourced PCA have led to the development of privacy-preserving schemes. Despite this, existing solutions face significant performance bottlenecks due to the iterative matrix computations involved in PCA, resulting in high overhead that limits their practicality. In this paper, we propose an efficient privacy-preserving outsourced PCA scheme. Specifically, we propose a secure Jacobi-EVD protocol, which improves efficiency by reducing nonlinear operations and iterations. Furthermore, by optimizing the matrix update operator in Jacobi-EVD using a hybrid protocol, we significantly reduce the communication overhead and communication rounds in iterative matrix computations. Security analysis demonstrates that our scheme preserves the privacy of data and PCA results. Performance evaluation shows our scheme significantly reduces 29.3× communication overhead compared to existing schemes.

Privacy-preserving data integration (PPDI) is a secure method to integrate datasets from different data sources while protecting the privacy of data. Existing PPDI work usually uses the outsourced framework and executes data integration through a cloud server. Due to the need to protect the privacy of the relations between IDs and associated data, the associated data must be encrypted or blinded before uploading to the cloud server, which leads to poor performance. For the efficient PPDI solution, we first carefully analyze the privacy goals of PPDI. After that, we adopt the distributed computing model, and then propose a multi-party PPDI protocol named DPPDI. Our scheme removes the overhead caused by encrypting associated data while protecting privacy, and realizes the outer join functionality and arbitrary combination of data sources. Besides, to avoid dropping records when duplicate IDs exist, we propose a method embedded into the PPDI protocol to handle duplicate IDs. Finally, we conduct extensive experiments to evaluate our scheme's performance, and the result shows that our scheme outperforms previous PPDI schemes.

Federated learning addresses the issue of data silo in machine learning. However, in practical applications, it still encounters challenges such as privacy leakage and communication bottleneck. Previous studies have proposed two main technologies to these challenges: secure aggregation to preserve privacy and Top-k gradient sparsification to reduce communication overhead, respectively. However, for both privacy preservation and communication efficiency, combining these two technologies results in compatibility issues and additional privacy leakage. In this paper, we propose a secure aggregation protocol with Top-k sparsification to achieve secure and efficient federated learning. We employ a differential privacy perturbation mechanism to protect Top-k features, thus preventing client's privacy leakage. Additionally, we design a sparse communication graph to ensure compatibility between secure aggregation and Top-k sparsification perturbed by differential privacy. We prove that our protocol protects Top-k features and conduct extensive experiments to evaluate its performance, which shows a significant reduction in the communication overhead compared to traditional secure aggregation protocols.

Federated learning can support multiple clients to train a global model with the assistance of the central server without sharing raw clients' data. However, the gradients uploaded by clients still compromise privacy and federated learning is vulnerable to poisoning attacks by malicious clients. Previous studies have focused on privacy preservation or defense against poisoning attacks, respectively. In practice, privacy preservation further increases the difficulty of defending against poisoning attacks, leading to the problem of severe degradation of model accuracy under strong privacy preservation. In this paper, we propose a lightweight, robust, and privacy-preserving federated learning scheme that can effectively resist poisoning attacks under strong privacy protection and prevent model accuracy degradation. We employ a differential privacy technique that adds noise to the upload gradient to protect data privacy. To address the problem that it is difficult to detect malicious clients under strong privacy preservation, we design a probabilistic grouping mechanism based on trust scores to support the detection of malicious clients. We conduct extensive experiments to evaluate our scheme, and the results show that our scheme can resist different types of poisoning attacks under strong privacy preservation, thus improving the accuracy of the model.

Spatial transcriptomics, by capturing both gene expression and spatial information, holds great promise for unraveling the complex organization of tissues. In this study, we introduce SpaICL, an image-guided curriculum strategy-based graph contrastive learning framework for spatial transcriptomics clustering. SpaICL integrates gene expression, spatial coordinates, and histological image features to construct a low-dimensional latent representation that enhances the de-lineation of spatial functional domains. The model employs a complementary masking strategy and a shared graph neural network encoder to generate dual embeddings, while a dual cross-attention mechanism aligns local and global features across multiple modalities. Additionally, the curriculum learning module further facilitates the gradual integration of neighborhood information, effectively mitigating the over-smoothing issues associated with fixed adjacency matrices. We evaluated the performance of SpaICL on five benchmark spatial transcriptomics datasets, achieving superior results compared to existing baseline methods. Moreover, SpaICL demonstrates significant potential in downstream analytical applications. The code of SpaICL is available at https://github.com/wenwenmin/SpaICL.

Mobile users often prefer to sense only a subset of tasks based on their preferences or physical conditions, which distinguishes sparse crowdsensing from traditional crowdsensing. Sparse crowdsensing not only introduces a potential risk of privacy leakage regarding users’ preferences or conditions—due to the revelation of specific sensed objects—but also results in reduced accuracy of truth estimation. To address these challenges, we propose a Privacy-Preserving Truth Discovery (PPTD) scheme, named S-PPTD, that enables accurate and efficient PPTD for sparse crowdsensing. Our approach leverages edge nodes to geographically group users and introduces an effective padding strategy based on Bloom filters and mixed secret sharing. This strategy allows users to obfuscate the objects they sense, preventing adversaries from determining the specific objects being sensed. To improve accuracy, we design new protocols for precise and efficient approximation of nonlinear functions, enabling the use of commonly applied kernel functions to capture spatial and temporal correlations between objects, and incorporate these into the truth estimation process. Through extensive experiments and security analysis, we demonstrate that S-PPTD is secure, accurate, and efficient in the context of sparse mobile crowdsensing.

Privacy-preserving truth discovery (PPTD) enables the crowdsensing platform to extract reliable inferred truths from unreliable user sensory data. While mobile crowdsensing systems have driven the emergence of many applications, continuously extracting inferred truths of evolving objects over streaming data (continuous PPTD) remains a challenge. Most existing works focus on static scenarios and cannot handle the new challenges in continuous PPTD, such as accuracy decrease, user dynamics, real-time requirements, and outliers. To address these challenges, we present PTET, a PPTD framework for continuous PPTD. By mining evolving patterns, PTET extracts accurate inferred truths of evolving objects even when some epochs lack sufficient user sensory data. PTET ensures the privacy of both users and data requesters while achieving high accuracy. Furthermore, we present PTET-P for practical applications. It employs a virtual user combined with evolving patterns to effectively eliminate the impact of user dynamics in continuous PPTD. Meanwhile, PTET-P achieves “immediate on-arrival processing” to improve real-time performance significantly. In addition, we address the outliers problem with the help of evolving patterns. We provide security analysis to prove that our frameworks protect the privacy of both users and data requesters. Extensive experiments demonstrate that our frameworks dramatically outperform the existing schemes in extracting inferred truths of evolving objects in continuous PPTD.

Crowdsourcing has emerged as an effective paradigm for large-scale data collection and statistical analysis. However, the paramount concern about worker privacy has driven the development of privacy-preserving statistical analysis methods. We propose PSAC, a novel framework that leverages histograms to facilitate privacy-preserving statistical analysis in crowdsourcing. PSAC integrates secure statistical analysis protocols based on homomorphic encryption and secure two-party computation, addressing the limitations of a single cryptographic technique. It introduces innovative algorithms using histograms for statistical operations, including functions such as quantile estimation, outlier elimination, contingency table construction for$\chi ^{2}$test, and the Mann-Whitney$U$test. These algorithms exhibit minimal overhead growth with respect to data volume, demonstrating exceptional scalability for large numbers of data. Moreover, through a key-separation design, PSAC ensures that only the requester can decrypt the final results independently, even if the ciphertexts of data are exposed. Comprehensive evaluations validate the security, efficiency, and scalability of the PSAC framework.

Building encrypted databases has been a long-standing challenge in the field of database security. In recent years, Structured Encryption (STE) has emerged as a promising approach to constructing encrypted databases, striking a balance between security and efficiency. Although existing STE-based encrypted database systems achieve high efficiency in query processing, all these schemes struggle to support rich queries with minimal information leakage. In this paper, we present a new STE-based encrypted database system, named Filter-integrated Encrypted Database (FinEDB), which supports exact-match and range queries, conjunctive queries and join operations, while maintaining limited information leakage. We first design a novel secure inverted index to avoid storage overhead blow-up when extending to support rich query capabilities. Then, we integrate Binary Fuse filters into our proposed inverted index to enable efficient query processing. By leveraging the homomorphic property of Binary Fuse filters, our approach leaks less information than existing STE-based solutions. Besides, we provide rigorous proof for our proposed scheme under the simulation paradigm. To evaluate the performance, we implement the prototype of FinEDB and compare it with the baseline STE-based scheme. Experiment results demonstrate that FinEDB is practical and can support rich queries on real-world databases.

Clustering is a crucial unsupervised machine learning algorithm extensively used in various practical applications, such as patient refinement and fraud detection, which often involve vertically distributed data across multiple data centers. However, sharing datasets directly is typically prohibited under GDPR due to potential privacy breaches. Therefore, privacy-preserving joint clustering for vertically distributed datasets is highly desired. In this paper, we propose Privacy-Preserving Vertically Federated Clustering (PPVFC), a solution that not only achieves this goal but also significantly reduces computational and communication overhead for each data owner (DO). Unlike most previous works that achieve the goal with a single privacy-enhancing technology, PPVFC jointly leverages multiparty homomorphic encryption (MHE) and multiparty computation (MPC) to efficiently interleave communication-lightweight homomorphic computations on the local dataset with operations over collectively secret-shared intermediate data. Specifically, we design a coefficient-wise encoding for MHE to pack large datasets and minimize communication costs. Additionally, we develop a round-efficient bit extraction protocol for determining the minimum distance. Through extensive experiments and security analysis, we demonstrate the practical performance and robust security guarantees of PPVFC.

Privacy-preserving statistical analysis enables the data center to analyze datasets from multiple data owners, extracting valuable insights while safeguarding privacy. However, the observation of microdata involvement in various analysis tasks within the data center can indirectly lead to privacy breaches. For instance, when the data center observes microdata involved in a disease-related task, it may reveal information about the corresponding user’s disease. Existing schemes process the entire dataset for each analysis task to prevent privacy breaches, resulting in significant redundancy overhead due to the large amount of task-irrelevant data involved in processing. In this paper, we propose FDC, which can protect privacy and effectively reduce the redundancy overhead. It frees the data center from huge redundancy overhead. Specifically, we propose a co-design of local differential privacy and multiparty computation with preprocessing by the data owner. This design enables the data center to process only task-relevant and LDP noise-induced microdata instead of the entire dataset while maintaining analysis results without accuracy loss. In some scenarios where preprocessing by the data owner is unfeasible, we present a data center-assisted method to complete preprocessing within the data center. Additionally, we design and optimize a secure shuffle protocol within this method. Finally, we implement and evaluate FDC using the aggregation task as a baseline. With different proportions of task-relevant microdata, experimental results show that the runtime of FDC is 2~11x faster than existing schemes on LAN and 2~22x on WAN, and the communication overhead is up to 3~153x lower.

The partial ordered data is typically used to describe the order of some elements within a set, and it widely exists in various fields, such as clinical investigations, preference ranking and voting. However, the collection of partial ordered data poses critical privacy concerns about abusing records to infer individuals’ identities and preferences. To solve this problem, this paper proposes a distribution analysis method for partial ordered data with local differential privacy (LDP). The private information of partial ordered data includes whether an element is associated with a partial order relation and either a relation is preceding or succeeding. To preserve privacy, we perturb partial ordered data by randomly responding raw data or the data with mapped elements. This makes it impossible to distinguish whether any element has a partial order relationship with other elements and what kind of partial order relationship exists. To maintain the logicality of partial ordered data, we utilize the transitivity of partial orders to distinguish between direct and indirect orders in the perturbation. The inherent properties of partial orders are still satisfied after perturbation, which reduces the possibility of servers inferring the raw data through logical errors. Moreover, we theoretically analyze the error bound and prove the security of our work. Extensive experimental results on synthetic and real-world datasets demonstrate that our scheme achieves better utility than existing state-of-the-art approaches.

Cloud-based Support Vector Machine (SVM) is a powerful technique for decision-assistance service. However, training data and models of SVM contain sensitive information, outsourcing these data to clouds may lead to severe privacy leakage. To address the privacy issue of SVM, many works focus on outsourced privacy-preserving SVM training. However, these works cannot support training SVM with non-linear kernel. This limitation renders these methods impractical for real-world scenarios where datasets are usually non-linear. In this paper, we propose a privacy-preserving SVM training scheme with support to non-linear kernel. Specifically, we redesign a gradient descent for SVM with kernel, which supports efficient kernel SVM training. We design basic computation protocols using secret sharing to achieve privacy preservation in outsourced SVM training. Additionally, we construct an incremental learning approach to support continuous data inflow. This approach is capable of reducing computational overhead significantly in practical scenario. Security analysis and efficiency evaluation illustrate that our proposed scheme achieves superior accuracy and less computation overhead compared to existing works, while also preserving privacy of training data and trained SVM model.

Truth discovery is an effective tool to infer true information from multi-source data and has been widely applied in mobile crowdsensing systems. In some specific scenarios, the sensory data are collected in a streaming fashion with time-varying information, and the server should update the truth in time. Under such circumstances, local differential privacy-based mechanism can satisfy the requirement of real-time processing properly while keeping the privacy of sensory data. However, directly applying local differential privacy to handle streaming data will disclose the long-term potential privacy and decrease the accuracy. To address these problems, we propose an incentive-based privacy-preserving truth discovery framework over streaming data. Firstly, we adopt the sequential composition theorem of w-event privacy to protect workers' long-term privacy. Second, we design an incentive mechanism to improve the submitted data utility and thus avoid the decrease in accuracy. In this way, our scheme ensures that workers submit more accurate data while their global privacy is still guaranteed. Finally, we prove our scheme satisfies w-event (∊, δ) differential privacy and theoretically analyze the result utility. Extensive experiments also demonstrate the effectiveness of our incentive mechanism.

The world is entering a new era with ubiquitous connectivity among billions of humans and machines, i.e., the sixth-generation (6G) massive Internet of Things (IoT). Radar and communication need to be integrated into this system to achieve target detection and massive connectivity. In this work, we first develop a shape-adaptive antenna array composed of multiple subarrays spaced at regular intervals. The shape-adaptive antenna array uses flexible material that enables changes in the physical structure to achieve adaptive gain. Subsequently, we investigate the micromovement of IoT targets, such as the micro-motion characteristics and micro-Doppler effects of rotary-wing drones. The simulation results demonstrate the effectiveness and efficiency of the proposed schemes for detecting unmanned aerial vehicles (UAVs) in an IoT scenario.

With the rapid development of 5G, intelligent vehicles have gradually entered the market. Obstacle detection is a very important part in the realization of intelligent vehicle. Millimeter wave radar is an important perceptron for vehicle obstacle detection. In order to avoid the problem that millimeter wave is seriously blocked by metal objects, and to meet the vehicle road cooperation mechanism, an active obstacle detection method based on millimeter wave radar base station is proposed, which greatly improves the detection range of radar The obstacle detection can realize the sensing and detection of more than 200 meters, and the position information of the small ball model can be clearly seen through the two-dimensional imaging of radar echo data obtained by simulation. Through the communication between the base station and the intelligent car and the car signal processing system, the obstacle detection function can be realized.

Radar communication integration is a system with the function of target detection and information transmission, which is an important development direction in recent years. The spectrum efficiency of integrated system has been the focus of research. This paper designs a radar communication integrated signal based on 64QAM-LFM, and makes theoretical and simulation analysis from the aspects of communication performance and radar performance. The results show that 64QAM-LFM signal is a radar communication integrated signal with large spectrum efficiency, high range resolution and velocity resolution.

Ride-hailing is a favored vehicular service model where drivers can deliver convenient rides to waiting riders via responding to a road-side unit or a ride-hailing service provider. However, previous works did not consider the order-linking function where a rider Cathy waving for a ride will be matched to a driver Bob in service with rider Alice whose destination is close to the start point of Cathy. Furthermore, a malicious matching executor could collude with an appointed driver to interfere with the matching process, which causes service unfairness and has not been addressed before. To mitigate these limitations, we first propose a privacy-preserving ride-hailing scheme OLink with the verifiable order-linking property. Specifically, we adopt road network partitioning and range query to achieve basic user matching. The user matching process supports range conditions and protects users' privacy. Next, a Proof-of-Linking protocol is designed based on the zero-knowledge succinct non-interactive argument of knowledge, zero-knowledge proof, and Bloom filters to enable the driver in service to generate three consecutive proofs for linking a current order to the next rider's order in advance; the proofs will be released such that anyone can verify the proofs and matching fairness is guaranteed. Finally, we formally prove the privacy and security of OLink, and then evaluate its performance with PySNARK to demonstrate feasibility and efficiency.

Unmanned Aerial Vehicles (UAVs) have been widely used in fifth-generation (5G) and the Internet of Things (IoT) because they can carry devices for communications and IoT and fly in a flexible and controllable manner. At present, in flight control, the UAV is mainly positioned by satellite. However, it may be interfered by the strong electromagnetic deception signal. A method for UAV assisted positioning is needed. In this work, we propose a new method of using radar to position the UAV. The range resolution capability of the wideband radar is used to acquire the target position in the detection area. The time-frequency analysis method is used to analyze the micro-Doppler effect generated by the rotation of the UAV rotor, and to identify whether the detected target is a UAV. The cepstrum method is used to estimate the feature data of the UAV such as the number of rotors and the speed, then the UAV is classified by the obtained feature data. The simulation results of the number of rotors, the rotation speed of each rotor and the position of the UAV is shown in the article.

Unmanned aerial vehicles (UAVs) have broad application potential for the Internet of Things (IoT) due to their small size, low cost, and flexible control. At present, the main positioning method for UAVs is the use of GPS. However, GPS positioning may be affected by stronger electromagnetic signals from spoofing attacks. In this study, a radar-assisted positioning method based on 5G millimeter waves is proposed. In 5G end-to-end network slices, the rotors of UAVs can be detected and identified by deploying 5G millimeter wave radar. High-resolution range profile (HRRP) is used to obtain the UAV location in the detection zone. Micro-Doppler characteristics are used to identify the UAVs and the cepstrum method is used to extract the number and speed information of the UAV rotor. The sinusoidal frequency modulation (SFM) parameter optimization method is used to separate multiple UAVs. The proposed method provides information on the number of UAVs, the position of the UAV, the number of rotors, and the rotation speed of each rotor. The simulation results show that the proposed radar detection method is well suited for UAV detection and identification and provides a valid GPS-independent method for UAV tracking.

UAVs (Unmanned Aerial Vehicles), also called drones, have drawn the attention of researchers owing to its flexibility, threatening and enormous application value. The construction of 5G network brings a new direction of detecting, identifying, and managing UAVs based on the native cloud architecture. In 5G end-to-end network slices, rotor UAVs are detected and identified by deploying 5G millimeter waves and using a joint algorithm, the improved short-time Fourier transform (STFT) and based on Bessel function base. For one-rotor UAV, the use of STFT following conjugation of sinusoidal frequency modulation (SFM) radar echo data based on millimeter wave doubles the recognition effect compared with the unconjugated processing. For multi-rotors UAV, the number of rotors and the length and rotational speed of each rotor are effectively identified through projection on the SFM data and the introduction of k order Bessel function. According to the results of automatic identification of UAVs by 5G native cloud architecture, the high bandwidth and low delay of 5G network provide a reliable basis for the resolution. Because of good robustness of the Bessel function, it provides an effective solution for the detection, identification and management of UAVs by 5G millimeter wave radar.