Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without direct data sharing. However, existing frameworks struggle to simultaneously ensure strong privacy protection and robustness against adversarial participants. Although numerous robust aggregation algorithms can mitigate Byzantine attacks and various privacy-preserving approaches have been developed, achieving both objectives within a unified and efficient framework remains challenging. To address this compatibility gap, we propose a generic privacy-preserving FL framework that integrates robust aggregation with cryptographic privacy guarantees through secure two-party computation under a dual-server architecture. In our design, two non-colluding servers collaboratively execute the aggregation process, ensuring that sensitive client updates remain confidential while enabling the application of diverse robust aggregation strategies. The framework is flexible and supports a wide range of aggregation algorithms, such as Multi-Krum, Median, and Mean-based methods, under a consistent security model. We implement the proposed system and conduct extensive experiments on multiple benchmark datasets. Experimental results demonstrate that our framework preserves the effectiveness of existing robust aggregation algorithms while maintaining acceptable runtime and communication overhead compared with standard FL baselines. These findings confirm that the proposed approach provides a practical and balanced solution to the dual challenges of privacy protection and Byzantine robustness, offering a versatile foundation for secure and trustworthy FL in adversarial environments.

The integration of physical and cyber networks can be a double-edged dynamic. While it accelerates the dissemination of positive information, it also intensifies the diffusion of negative information. A clear understanding of the stochastic dynamics of competitive information dissemination is essential. It enables effective control strategies to curb the spread of negative information and maintain social stability. However, designing and controlling the dissemination of competitive information in cyber–physical integrated networks (CPINs) is challenging due to competition and stochasticity. Moreover, the heterogeneity of CPINs further aggravates this problem. To address this, we propose a competitive information dissemination method to capture and control the stochastic dynamics in CPINs. Specifically, we build a CPIN model to characterize the heterogeneity between physical and cyber networks. Furthermore, we develop a temporal point process-based competitive information dissemination model (TPP-CIDM) that captures the stochastic evolution of both positive and negative information. This dissemination model quantifies the stochastic dynamics by computing the probability distribution of the sizes of competitive information, reducing biases inherent in deterministic solutions. Finally, we design an event-driven optimal control (EOC) strategy to dynamically modulate the intervention intensity. The intervention optimization problem is formulated to maximize utility under cost constraints, and a heuristic solution is provided. Numerical simulations on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.

Privacy-preserving biometric authentication (PPBA) enables client authentication without revealing sensitive bio-metric data, addressing privacy and security concerns. Many studies have proposed efficient cryptographic solutions to this problem based on secure multi-party computation, typically assuming a semi-honest adversary model, where all parties follow the protocol but may try to learn additional information. However, this assumption often falls short in real-world scenarios, where adversaries may behave maliciously and actively deviate from the protocol. In this paper, we propose, implement, and evaluate FLAME, a Flexible and Lightweight biometric Authentication scheme designed for a Malicious Environment. By hybridizing lightweight secret-sharing-family primitives within two-party computation, FLAME carefully designs a line of supporting protocols that incorporate integrity checks with rationally extra overhead. Additionally, FLAME enables server-side authentication with various similarity metrics through a crossmetric-compatible design, enhancing flexibility and robustness without requiring any changes to the server-side process. A rigorous theoretical analysis validates the correctness, security, and efficiency of FLAME. Extensive experiments highlight FLAME's superior efficiency, with a communication reduction by 97.61x 110.13x and a speedup of 2.72x 2.82x (resp. 6.58x 8.51x) in a LAN (resp. WAN) environment, when compared to the state-of-the-art work.

Mobile crowdsourcing is a new computing paradigm that enables outsourcing computation tasks to mobile crowd nodes by means of offloading the tasks from the user to a mobile edge computing (MEC) server. This article studies the problem of scheduling security-critical tasks of crowdsourcing applications in a multiserver MEC environment. We formulate this scheduling problem as an integer program and propose a family of convergent grey wolf optimizer (CGWO) metaheuristic algorithms to seek for the best scheduling solutions. Our proposed CGWO uses a task permutation to represent a candidate solution to the formulated scheduling problem, and employs a probability-based mapping scheme to map each search agent in grey wolf optimizer (GWO) onto a valid task permutation. We introduce a new position update strategy for generating the next generation of grey wolf population after each round of search. With this strategy, we prove our proposed CGWO guarantees its convergence to the global best solution. More importantly, we provide a thorough analysis on the movement trajectories of grey wolves during the evolutionary procedure, in order to determine appropriate parameter values such that CGWO would not be trapped in local optima. Experimental results justify the superiority of CGWO metaheuristics over the standard GWO in solving the crowdsourcing task scheduling problem.

Location privacy protection (LPP) has become a key concern during mobile crowdsourcing (MCS) task allocation. Existing LPP mechanisms for MCS applications mainly focus on two-dimensional (2D) plane scenarios or directly apply 2D techniques into three-dimensional (3D) space scenarios, leaving the height dimension of 3D geolocation vulnerable to privacy breaches. To facilitate the LPP in 3D MCS, we propose a learning-based geo-perturbation mechanism using 3D geo-indistinguishability (3D-GI). In this mechanism, we first define an optimization objective to balance location privacy and MCS server profit, making it adaptable to different types of MCS applications. Then, we adopt the Asynchronous Advantage Actor-Critic (A3C) algorithm to design a reinforcement learning (RL)-based approach without knowing the accurate system and attack models. This approach enables us to derive the optimal perturbation policy in continuous policy space and accelerates the learning speed using asynchronous multi-thread training. Simulation results demonstrate that the proposed mechanism can better balance location privacy and server profit in 3D MCS applications compared to existing benchmarks.

Federated learning is a machine learning prgadigm that enables the collaborative learning among clients while keeping the privacy of clients’ data. Federated multitask learning (FMTL) deals with the statistic challenge of non-independent and identically distributed (IID) data by training a personalized model for each client, and yet requires all the clients to be always online in each training round. To eliminate the limitation of full-participation, we explore multitask learning associated with model clustering, and first propose a clustered FMTL to achieve the multual-task learning on non-IID data, while simultaneously improving the communication efficiency and the model accuracy. To enhance its privacy, we adopt a general dual-server architecture and further propose a secure clustered FMTL by designing a series of secure two-party computation protocols. The convergence analysis and security analysis is conducted to prove the correctness and security of our methods. Numeric evaluation on public data sets validates that our methods are superior to state-of-the-art methods in dealing with non-IID data while protecting the privacy.

Towards the large amount of data generated everyday, data selective sharing and acquisition is one of the most significant data services in cloud-based systems, which enables data owners to selectively share their data to some particular users, and users to selectively acquire some interested data. However, it is challenging to protect data security and user privacy during data selective sharing and selective acquisition, because cloud servers are curious about the data or user’s interests, and even send data to some unauthorized users or some uninterested users. In this paper, we propose an efficient and provably secure Data selective Sharing and Acquisition (${\sf DSA}$) scheme for cloud-based systems. Specifically, we first formulate a generic data selective sharing and acquisition problem in cloud-based systems by identifying several design goals in terms of correctness, soundness, security and efficiency. Then, we propose the${\sf DSA}$scheme to enable data owners to control the access of their data in a fine-grained manner, and enable users to refine the data acquisition without revealing their interests. Technically, a brand new cryptographic framework is developed to integrate attribute-based encryption with searchable encryption. Finally, we prove that the proposed${\sf DSA}$scheme is correct, sound, secure in the random oracle model, and efficient in practice.

The rapid growth of the Internet of Vehicles (IoV) paradigm sparks the generation of large volumes of distributed data at vehicles, which can be harnessed to build models for intelligent applications. Federated learning has recently received wide attentions, which allows model training over distributed datasets without requiring raw datasets to be shared out. However, federated learning is known to be vulnerable to poisoning attacks, where malicious clients may manipulate the local datasets or model updates to corrupt the global model. Such attacks have to be countered when federated learning is adopted in IoV systems, given that the training process is distributed among a large number of vehicles in an open environment. In addition, IoV systems present a hierarchical architecture in practice where other types of nodes sit between the cloud server and vehicles, allowing intermediate aggregation for reducing overall training latency. Yet the intermediate aggregation nodes may also pose threats. In this paper, we propose a robust hierarchical federated learning framework named RoHFL, which allows hierarchical federated learning to be suitably applied in the IoV with robustness against poisoning attacks. We develop a robust model aggregation scheme that contains a logarithm-based normalization mechanism to cope with scaled gradients from malicious vehicles. We integrate the notion of reputation into the aggregation process and develop a scheme for reputation updating. We provide a formal analysis of RoHFL’s convergence guarantees. Experiment results over several popular datasets demonstrate the promising performance of RoHFL, which is superior to prior work in the robustness against poisoning attacks.

Compound screening is a key step in the development of new drugs. Current high-throughput screening methods cannot be widely adopted by laboratories due to their expensive equipment and low efficiency. The booming deep learning in recent years has provided a new answer to this question. The message passing neural network (MPNN) can directly predict molecular properties from molecular structure so that compound screening can be completed without experimentation. In the face of large-scale molecular data, outsourcing this task to a professional cloud server can further accelerate prediction efficiency and reduce costs. In order to solve the privacy protection problem of computing on cloud servers, we propose a 3-party molecular structure properties inference privacy protection framework SecMPNN based on additive secret sharing. We design brand-new cryptographic protocols to ensure the privacy and security in the prediction process, and through experiments show that the single inference time of our protocol on different networks is 40.85% faster than CRYPTEN and 18.5% faster than SecureNN.

Federated learning (FL) is a privacy-preserving distributed machine learning architecture to solve the problem of data silos. While FL is proposed to protect data security, it still faces security challenges. Backdoor attacks are potential threats in FL and aim to manipulate the model performance on chosen backdoor tasks by injecting adversarial triggers. As a more insidious variant of backdoor attacks, distributed backdoor attacks decompose the same global trigger into multiple local patterns and respectively assign them to different attackers. In this paper, we study deep into the entire training process of current distributed backdoor attack (DBA) and propose a cooperative DBA method for non-IID FL to break through existing defenses. To bypass the cosine similarity detection, we design an update rotation and scaling technique based on two independent training to well disguise malicious updates among benign updates. We conduct an exhaustive experiment to evaluate the performance of our proposed method under the state-of-the-art defenses. The experimental results show that it is much more stealthy than the current DBA method while maintaining the high backdoor attack intensity.

Public auditing schemes for cloud storage systems have been extensively explored with the increasing importance of data integrity. A third-party auditor (TPA) is introduced in public auditing schemes to verify the integrity of outsourced data on behalf of users. To resist malicious TPAs, many blockchain-based public verification schemes have been proposed. However, existing auditing schemes rely on a centralized TPA, and they are vulnerable to tempting auditors who may collude with malicious blockchain miners to produce biased auditing results. In this article, we propose a blockchain-based decentralized public auditing (BDPA) scheme by utilizing a decentralized blockchain network to undertake the responsibility of a centralized TPA, and also mitigate the influence of tempting auditors and malicious blockchain miners by taking the concept of decentralized autonomous organization (DAO). A detailed security analysis shows that BDPA can preserve data integrity against tempting auditors and malicious blockchain miners. A comprehensive performance evaluation demonstrates that BDPA is feasible and scalable.

By providing rich context of lane information on roads, lane-level maps play a vital role in intelligent transportation systems. Since Global Positioning Systems (GPS) have been widely applied to vehicles, vehicle-based crowdsourcing offers an economical way to the lane-level map building by collecting and analyzing the GPS trajectories of vehicles. However, existing works cannot directly extract lane-level road information from raw and interleaved crowdsourcing trajectories, and moreover they are time-consuming and inaccurate. In this article, we propose a lane-level map building scheme, which can directly extract lane-level road information from raw crowdsourcing GPS trajectories with both efficiency and accuracy improvement. Consider the global similarity between trajectories, we design an efficient trajectory segmentation and clustering algorithm based on improved discrete Fréchet distance and entropy theory, which can directly and accurately deal with the interleaved and messy trajectories. To improve the efficiency, we employ the Least Square Estimate (LSE) to constrain Gaussian Mixture Model (GMM) and design an efficient and accurate lane-level road information extraction algorithm. Comprehensive comparative experiments and performance evaluation on a real-world trajectory dataset show that the proposed scheme outperforms the state-of-the-art works in terms of both efficiency and accuracy.

Online Ride Hailing (ORH) services enable a rider to request a taxi via a smartphone app in real time. When using ORH services, users (including riders and taxis) have to submit their locations to the ORH server. With received locations, the ORH server makes online ride matching between riders and taxis. There are serious privacy concerns for users to reveal location information to ORH servers. In this article, we propose an efficient and privacy-preserving ride matching scheme for ORH services, named EPRide. EPRide can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location information of both taxis and riders against ORH servers or other curious servers. In EPRide, we propose an efficient exact shortest road distance computation approach over encrypted data, which converts road distance computation into Hamming distance computation over packed ciphertexts by using road network hypercube embedding and somewhat homomorphic encryption. Meanwhile, we design a secure comparison protocol, which efficiently compares encrypted distances in parallel by using ciphertexts blinding and packing, without leaking any distance. Theoretical analysis and experimental evaluations show that EPRide is secure, accurate and efficient.

Federated learning (FL) is a framework of distributed machine learning, which aims to protect data privacy by transferring parameters instead of private data from local clients. Compared with the typical cloud-client architecture, applying FL on a cloud-edge-client hierarchical architecture could train the model faster and achieve better communication-computation trade-offs. However, hierarchical federated learning (HFL) still suffers from privacy leakage by analyzing uploaded parameters from clients or edge servers. To address this problem, we propose a privacy-preserving scheme based on the theory of local differential privacy (LDP), where adding the noise to the shared model parameters before uploading them to edge and cloud servers. According to our analysis by the moment accounting, the proposed algorithm can realize the strict differential privacy guarantee for the layers of clients and edge servers with adjustable privacy protection levels. We evaluate its performance based on the image classification tasks, and the result demonstrates that our theoretical analyses are consistent with simulations.

Blockchain has attracted wide attention in both industry and academia due to its decentralized and anti-tamper characteristics. To query the blockchain data, all the transactions in the blockchain must be iterated one by one, which is quite inefficient. Existing solutions improve the query efficiency by exporting transactions to external databases, and yet they incur the incompleteness and incorrectness of query results. To avoid above weaknesses, we propose an efficient query method based on the internal blockchain database and apply it to the construction engineering management to solve the problems of information management. In our design, we construct a dual-index based on the B+ tree and the key-value pair through smart contracts, and it supports multiple query operations, such as range query and file-type query. We implement the designed method by simulating the blockchain testbed and the experimental results demonstrate the efficiency of our design.

With the development of Edge Computing (EC), security issues have raised concerns. Due to the unusual vulnera-bility of EC servers and the distributed nature of attack sources, it is a great challenge to efficiently and effectively defend against DDoS attacks. Existing detection solutions, based on the feedback of servers under the attacks, can incur high bandwidth costs and degradation of service performance. To address this problem, we propose a novel collaborative prediction framework, called CoWatch. Based on the distributed software-defined network (SDN), the CoWatch framework can collaboratively predict the DDoS attacks towards the EC servers and detect the attack flows near the attack source in time. To efficiently filter the suspicious flows in distributed SDN, we design an optimal threshold model by balancing the trade-off between collaboration efficiency and prediction effectiveness. We also explore the prototypical LSTM network to design an LSTM-based Collaborative Prediction (LCP) algorithm, which can effectively predict and detect DDoS attacks. Experiment results demonstrate the effectiveness of the prediction and detection of DDoS attacks and validate the efficiency of flow information synchronization in distributed SDN.

Federated Learning enables the collaborative learning in cross-client scenarios while keeping the clients' data local for privacy. The presence of non-IID data is one of major challenges in federated learning. To deal with this statistic challenge, federated multi-task learning considers the local training for each client as a single task. However, all the clients must participate in each training round, and it is inapplicable to mobile or IOT devices with constrained communication capability. To achieve the communication-efficiency and high accuracy with non-IID data, we propose a clustered federated multi-task learning by exploring client clustering and multi-task learning. We measure the similarities of local data among clients indirectly through their models' parameters, and design a client clustering strategy to enable clients with similar data distribution into a same group. The limitation of full-participation can be eliminated through the way of model training for groups instead of individual clients. The convergence analysis and experimental evaluation on real-world datasets shows that our work outperforms the basic federated learning in accuracy and is also more communication-efficient than the existing federated multi-task learning.

Smart contracts are a landmark achievement of blockchain technology 2.0 and are widely adopted in various applications. However, smart contracts are not always secure and there are various vulnerabilities. The reentrancy vulnerability is one of most serious vulnerabilities, and it has caused huge economic losses. Although many methods have been proposed to detect reentrancy vulnerabilities, they all have high false positives. To deal with this problem, we propose a symbolic execution-based detection tool for reentrancy vulnerabilities of smart contracts at the EVM bytecode level. By analyzing a large number of real-world smart contracts, we conclude main patterns of false positives and design five effective path filters to eliminate false positives. We evaluate its performance on real-world datasets in comparison with the state-of-the-art works, and the results show that our tool is more effective in the detection of reentrancy vulnerabilities.

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including Server-Local Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the whole system. The proposed scheme is provably secure in the random oracle model under the Decisional q-Combined Bilinear Diffie-Hellman (q-DCDBH) assumption. Comprehensive theoretical analysis and detailed simulation results show that the proposed scheme outperforms the state-of-the-art work.

Online Ride Hailing (ORH) has extensively made our trip more convenient. With mobile devices, riders can request taxis through ORH systems in a short time. However, to enjoy ORH services, users need to submit their location information to ORH systems, which raises serious privacy concerns. In this paper, we study the privacy leakage of online ridesharing matching, a more complex and economy ORH service that allows riders to share rides with others, and propose a privacy-preserving shared ride matching scheme, called PSRide. PSRide can find the taxi with the minimum additional travel time to serve a new rider based on its existing schedule, while protecting the location privacy of both riders and taxis. In PSRide, we propose a zone-based minimum road travel time estimation approach and a secure comparison protocol to efficiently optimize the schedules of taxis for a new rider over encrypted data. We implement PSRide and analyze it thoroughly. Theoretical analysis and experimental evaluations show that PSRide is secure and efficient for ORH systems.

Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of convenient services in intelligent transportation systems, and yet vulnerable to various intrusion attacks. Intrusion detection systems (IDSs) can mitigate the security threats by detecting abnormal network behaviours. However, existing IDS solutions are limited to detect abnormal network behaviors under local sub-networks rather than the entire VANET. To address this problem, we utilize deep learning with generative adversarial networks and explore distributed SDN to design a collaborative intrusion detection system (CIDS) for VANETs, which enables multiple SDN controllers jointly train a global intrusion detection model for the entire network without directly exchanging their sub-network flows. We prove the correctness of our CIDS in both IID (Independent Identically Distribution) and non-IID situations, and also evaluate its performance through both theoretical analysis and experimental evaluation on a real-world dataset. Detailed experimental results validate that our CIDS is efficient and effective in intrusion detection for VANETs.

Crowdsourcing is a distributed computing paradigm that utilizes human intelligence or resources from a crowd of workers. Existing solutions of task recommendation in crowdsourcing may leak private and sensitive information about both tasks and workers. To protect privacy, information about tasks and workers should be encrypted before being outsourced to the crowdsourcing platform, which makes the task recommendation a challenging problem. In this paper, we propose a privacy-preserving task recommendation scheme (PPTR) for crowdsourcing, which achieves the task-worker matching while preserving both task privacy and worker privacy. In PPTR, we first exploit the polynomial function to express multiple keywords of task requirements and worker interests. Then, we design a key derivation method based on matrix decomposition, to realize the multi-keyword matching between multiple requesters and multiple workers. Through PPTR, user accountability and user revocation are achieved effectively and efficiently. Extensive privacy analysis and performance evaluation show that PPTR is secure and efficient.

To mitigate the issues of scalability and reliability in centralized SDN, distributed SDN has emerged. However, cyber attacks in distributed SDN become increasingly serious. Since each distributed SDN controller can only obtain the network flows of its sub-network, a single controller with the biased flow information cannot detect all types of attacks in the entire network and the overall detection is a challenge. To solve the biased flow problem, we propose a collaborative anomaly detection scheme in distributed SDN, which enables multiple SDN controllers jointly train a global detection model to identify cyber attacks. We evaluate its performance based on a real-world dataset and the results show that our scheme is efficient and accurate in cyber attack detection.

The imbalanced problem indicates that the data set is unevenly distributed, resulting in sub-optimal classifiers to recognize the minority class. Traditional solutions try to design new classifiers to solve this problem or balance the skewed data sets, the former is too costly while the latter has an uncertain effect on different combinations of classifiers and measurements. In this paper, we propose a reinforcement learning-based oversampling method, which can directly produce targeted samples according to the downstream classifiers and measurements. During training, our learning procedure introduces the classification information to the generation process. Moreover, as opposed to oversampling approaches, we have no assumption of the downstream classifiers and performance metrics, and the proposed has a wider application. We carry out experiments on 17 UCI and KEEL data sets, experimental results demonstrate the superior performance of our proposed method.

The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. Task matching or task subscription is one of indispensable services in crowdsourcing, but few mechanisms can achieve the expressive task subscription while protecting the privacy. In this paper, we focus on the privacy leaks and attacks during task subscription in crowdsourcing, and propose a privacy-preserving task subscription scheme with sybil detection, called SybSub. The SybSub scheme achieves the expressiveness of task subscription in the multisubscriber and multipublisher crowdsourcing while protecting the privacy of both subscribers and publishers against the semi-honest crowdsourcing service provider, and meanwhile supports the sybil attack detection against greedy subscribers. We implement the SybSub scheme and evaluate it thoroughly. Performance results validate that the SybSub scheme is efficient and feasible.

As an important application of the Internet of Things technologies, mobile healthcare crowd sensing (MHCS) still has challenging issues, such as privacy protection and efficiency. Quite recently in the IEEE Internet of Things Journal (DOI: 10.1109/JIOT.2018.2828463), Liuet al.proposed a large-scale concurrent data anonymous batch verification scheme for MHCS, claiming to provide batch authentication, nonrepudiation, and anonymity. However, after a close look at the scheme, we point out that the scheme suffers two types of signature forgery attacks and hence fails to achieve the claimed security properties. In addition, a reasonable and rigorous probability analysis indicates that the security reduction from the security of the scheme to the hardness of the computational Diffie–Hellman problem is invalid. We hope that similar design flaws can be avoided in future design of anonymous batch verification schemes for MHCS.

The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. In this paper, we focus on the privacy leaks and sybil attacks during the task matching, and propose a privacy-preserving task matching scheme, called SybMatch. The SybMatch scheme can simultaneously protect the privacy of publishers and subscribers against semi-honest crowdsourcing service provider, and meanwhile support the sybil detection against greedy subscribers and efficient user revocation. Detailed security analysis and thorough performance evaluation show that the SybMatch scheme is secure and efficient.

With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, but also achieves query traceability against dishonest or revoked workers. Detailed privacy analysis and thorough performance evaluation show that the proposed scheme is secure and feasible.

Many crowdsourcing platforms have been developed, which enable workers to complete a broad range of complex tasks published by task requesters. Existing task recommendation systems require sensitive information such as task content and interests of workers, which has raised serious privacy concerns. In order to preserve users' privacy in crowdsourcing, we propose a secure task recommendation scheme that achieves the preservation of task privacy and worker privacy simultaneously. Based on proxy cryptography, we realize the encrypted keyword-based matching between task specification and worker interest, and the encryption and decryption of task content, both in the multi-user environment. Moreover, user revocation is also supported. Through rigorous security analysis and performance evaluation, our scheme is secure and feasible.

In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user's search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.

Cloud computing becomes increasingly popular. To protect data privacy, sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. The existing searchable encryption schemes support only exact or fuzzy keyword search, not support semantics-based multi-keyword ranked search. In the real search scenario, it is quite common that cloud customers' searching input might be the synonyms of the predefined keywords, not the exact or fuzzy matching keywords due to the possible synonym substitution (reproduction of information content) and/or her lack of exact knowledge about the data. Therefore, synonym-based multi-keyword ranked search over encrypted cloud data remains a very challenging problem. In this paper, for the first time, we propose an effective approach to solve the problem of synonym-based multi-keyword ranked search over encrypted cloud data. We make contributions mainly in two aspects: synonym-based search for supporting synonym query and multi-keyword ranked search for achieving more accurate search result. Two secure schemes are proposed to meet privacy requirements in two threat models of known ciphertext model and known background model. In enhanced scheme, the sensitive frequency information can be well protected by introducing some dummy keywords, which is not adopted in basic scheme. We give security analysis to justify the correctness and privacy-preserving guarantee of the proposed schemes. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting synonym-based searching.