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.

Searchable symmetric encryption (SSE) is a technique that enables secure outsourcing of data to an untrusted cloud server without sacrificing search functionality. Recently, multi-user SSE schemes for data sharing, which support access control from various users, have gained attention. However, the access control mechanisms in existing schemes are not adequate for realistic data-sharing scenarios as they do not consider time constraints or only partially address them, making these mechanisms unsuitable for SSE schemes. To address this issue, we first highlight the importance of time constraints in multi-user SSE and propose a completely time-constrained SSE scheme under a two-server model. By taking advantage of the Lagrange interpolation and pre-computation, our proposed scheme enables searching over time-related encrypted data with owner-enforced time constraints. Additionally, we employ the blinding technique with the assistance of a semi-honest time server to ensure the completeness of time constraints, which is not guaranteed in existing works. Based on the leakage function, we prove the security of our proposed scheme in the simulation-based security model. Furthermore, extensive experiments demonstrate the practicality of our scheme in supporting time-constrained functions.

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.

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.

Set-valued data are commonly used to represent subsets of a universal set and are frequently utilized in online services, such as online shopping preferences, website browsing records, and recently visited places. By collecting set-valued data from users, service providers can perform statistical analysis to obtain a joint distribution of service usage data and subsequently learn the association between different kinds of set-valued data to improve the quality of service. However, collecting set-valued data raises privacy concerns about the potential misuse of records to infer individuals’ identities and preferences. Although some privacy-preserving aggregation mechanisms for set-valued data have been proposed, they have not yet achieved joint distribution analysis with high accuracy. In this paper, we propose a joint distribution analysis method for set-valued data with local differential privacy (LDP). We design a scalable perturbation mechanism under$\epsilon $-LDP by limiting the range of users’ responses in the collection process and cyclically shifting the set-valued data in an encoded uniform format, ensuring that the size of the universal set does not influence the accuracy of the results. Based on the perturbation method, we develop an analysis method to efficiently obtain association information between two sets. By performing specific bitwise operations on the perturbed data matrices, the computational overhead is linear with respect to the cardinality of the item set. In addition to theoretically analyzing the error bound and proving 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.

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.

Federated Learning (FL) enables multiple distributed clients to collaboratively train a model with owned datasets. To avoid the potential privacy threat in FL, researchers propose the DP-FL strategy, which utilizes differential privacy (DP) to add elaborate noise to the exchanged parameters to hide privacy information. DP-FL guarantees the privacy of FL at the cost of model performance degradation. To balance the trade-off between model accuracy and security, we propose a differentially private federated learning scheme with an adaptive noise mechanism. This is challenging, as the distributed nature of FL makes it difficult to appropriately estimate sensitivity, where sensitivity is a concept in DP that determines the scale of noise. To resolve this, we design a generic method for sensitivity estimates based on local and global historical information. We also provide instances on four commonly used optimizers to verify its effectiveness. The experiments on MNIST, FMNIST and CIFAR-10 convincingly prove that our proposed scheme achieves higher accuracy while keeping high-level privacy protection compared to prior works.

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.

Recently, there have been many discussions in mobile crowd-sensing about privacy-preserving truth discovery because of its ability to extract truthful information from noisy or biased sensory data without privacy breaches. However, in practical applications, users (referred to as workers) may report outliers due to device malfunction, malicious workers, etc. These outliers will dramatically impact the accuracy of the truth discovery result. Detecting outliers based on existing privacy preservation schemes will carry an intolerable overhead, dramatically reducing the system's availability. In this paper, we propose our privacy-preserving truth discovery scheme that can detect outliers. Specifically, we adopt an anonymous mechanism to achieve privacy preservation. Since the existing anonymous mechanisms require huge overhead and do not work correctly when some workers exit, they are difficult to be applied in mobile crowdsensing systems. We design a lightweight and robust anonymous mechanism based on the edge computing paradigm. In addition, we eliminate the impact of outliers through outlier detection to achieve robustness of truth discovery results. Finally, we demonstrate the security of our scheme through security analysis and the efficiency of our scheme in terms of computation and communication overhead through extensive experiments.

Truth discovery is an effective method to infer truthful information from a large amount of sensory data in mobile crowdsensing systems. Privacy-preserving truth discovery schemes require the cloud server not to access each worker's sensory data directly so that the privacy of sensory data can be preserved. In some specific applications such as sparse mobile crowdsensing, workers can only contribute sensory data on a small part of sensing tasks, implying that the information of which tasks are completed by a worker should also be preserved. However, existing privacy-preserving truth discovery schemes do not consider such sparse data scenarios in mobile crowdsensing systems. In this paper, we first identify the privacy issues in truth discovery when sensory data are sparse. To address these issues, we design a privacy-preserving truth discovery scheme by employing the additively homomorphic cryptosystem and additive secret sharing with two non-colluding servers. Through detailed analysis and extensive experiments, we demonstrate that our proposed scheme can satisfy strong privacy-preserving requirements with low computation and communication overhead.

With the proliferation of mobile and wearable devices, mobile crowdsensing (MCS) is becoming a new paradigm for data collection and analysis. To effectively identify truthful information from crowdsensed data without privacy leakage, privacy-preserving truth discovery (PPTD) has gained much attention recently. Existing works either didn't consider real-time applications over data streams or failed to achieve enough efficiency for a large group of workers. In this paper, we propose FPTD, a Fog-aided Privacy-preserving Truth Discovery framework which is secure and efficient in handling real-time applications with a large group of workers. To reduce overhead, we adopt cloud-fog computing architecture to divide the complete worker group into many smaller ones. Then we design a unique secure aggregation protocol SecAgg which can securely and efficiently aggregate inputs from workers in smaller groups. Finally, we give detailed construction of FPTD, an efficient truth discovery framework based on SecAgg for real-time applications. Through extensive experiments and security analysis, we demonstrate that both SecAgg and FPTD are secure and efficient.

Recently, truth discovery in crowdsensing systems has received considerable attention with its appealing features for extracting truthful information from multiple unreliable data sources. However, it also poses new challenges to the issues of privacy and security. On the one hand, workers' sensed data can be used to infer their privacy. On the other hand, workers may be selfish and lazy, especially in the Internet-of-Things environment, devices are usually resource constrained, so they may dishonestly execute the costly sensing task so as to reduce resource consumption, or even break the protocol to obtain illegal rewards. Although some privacy-preserving truth discovery schemes have been proposed, they still cannot achieve strong privacy protection while keeping efficiency on the worker side, and still has no efficient incentive mechanism to persuade workers to participate in the system operations. In this article, we propose an incentive-based privacy-preserving truth discovery framework, named InPPTD. By adopting the Paillier homomorphic cryptosystem and two noncolluding servers, InPPTD not only effectively protects workers' sensed data information but also preserves the privacy of these workers' weight information. Meanwhile, a weight-based incentive mechanism is introduced in InPPTD to reduce the number of lazy workers. Security and performance analysis shows that InPPTD can guarantee stronger security features, while also ensure efficiency in terms of computation and communication overhead.

Deep learning as a service has been widely deployed to utilize deep neural network models to provide prediction services. However, this raises privacy concerns since clients need to send sensitive information to servers. In this paper, we focus on the scenario where clients want to classify private images with a convolutional neural network model hosted in the server, while both parties keep their data private. We present FALCON, a fast and secure approach for CNN predictions based on fast Fourier Transform. Our solution enables linear layers of a CNN model to be evaluated simply and efficiently with fully homomorphic encryption. We also introduce the first efficient and privacy-preserving protocol for softmax function, which is an indispensable component in CNNs and has not yet been evaluated in previous work due to its high complexity.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

Secure data aggregation has been widely studied in the area of the smart grid. Many existing schemes have studied protecting user's privacy in data aggregation by using advanced cryptographic tools. However, they usually introduce a large computation burden to smart meters in limited computing power or require a trusted authority. How to ensure the efficiency on the user side while preserving user's privacy still has not been well addressed. In this article, we consider the scenario where there does not exist a trusted authority and users in the smart grid may dynamically change, and propose an efficient and robust data aggregation scheme without a trusted authority for the smart grid. Our proposed scheme not only ensures user's privacy and efficiency but also supports flexible dynamic user management with no need of involving a trusted authority. Analysis of security and performance shows that our scheme can guarantee stronger security features, while ensuring efficiency in terms of computation, communication, and storage overhead.