Federated learning is a machine learning paradigm through training on locally private data and aggregating local models to generate a federated model. However, due to the heterogeneity problems (data heterogeneity and model heterogeneity), federated learning suffers from convergence difficulties and excessive aggregation overhead on decentralized participants. Additionally, federated learning faces privacy concerns during the aggregation of local models. To this end, in this work, we propose FEDINF, an efficient and secure inference with federated participants. Specifically, FEDINF features the following characteristics. FEDINF overcomes convergence challenges through federated inference instead of federated training, which reduces computation and communication overhead. Moreover, we design secure computation protocols and aggregation mechanisms to measure contributions, and handle both data and model heterogeneity without sacrificing privacy. Results of experimental evaluations on common datasets demonstrate that the proposed FEDINF outperforms the existing federated learning approaches in terms of efficiency and heterogeneity.

The significance of maritime transportation highlights the need to enhance the efficiency of container terminals. This study addresses a challenge within maritime transportation, specifically the continuous berth allocation and time-variant quay crane assignment problem (C/T-V BACAP). We formulate a comprehensive mathematical model of C/T-V BACAP. To solve the problem, we propose an effective memetic algorithm with a heuristic decoding method, named HMA, which comprises three essential components: a three-stage heuristic decoding method, a clustering-based evolutionary strategy, and a target-guided local search operator. The three-stage heuristic decoding method guarantees solution feasibility and high quality through the entire optimization, allowing the following strategies to fully utilize their search capabilities. The clustering-based evolutionary strategy refines the search space and diversifies the promising candidates. Meanwhile, the target-guided local search operator rapidly optimizes the allocation for the challenging vessel. The experimental results demonstrate that the proposed algorithm delivers excellent performance, especially in handling large-scale instances (up to 60 vessels). Our proposed method outperforms the state-of-the-art BACAP algorithms by an average margin of 150% in terms of berth offset and waiting time in most problem instances.

Facial expression recognition is a technology that involves analyzing and interpreting human facial expressions to determine individual expressions or states. Mobile crowdsensing (MCS), a promising sensing paradigm, makes it easy to capture facial images and benefits facial expression recognition. Existing inference models for facial expression recognition usually rely on facial feature vectors or facial images, increasing privacy concerns about expression. For this reason, this paper proposes a privacy-preserving facial expression recognition scheme through MCS, named RAPOO, which falls in a client-server architecture. Roughly speaking, a user captures facial images using mobile devices and requests a recognition service provided by a cloud computing center. To protect the privacy of expressions, our approach focuses on designing secure computation protocols required by facial expression recognition necessarily, such as secure vector distance calculation and secure top-$k$query. These protocols enable facial expression recognition over encrypted data directly. To speed up the recognition and store encrypted feature vectors, a$k$-D tree data structure is introduced. The security analysis confirms that RAPOO effectively preserves the confidentiality of personal expressions. Extensive experimental evaluations show that our solution obtains a three-order-of-magnitude speedup in terms of computational overhead compared with the state-of-the-art.

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server needs to perform a process called ciphertext delegation (Sahai et al., CRYPTO’12) that periodically updates the timestamp for all ciphertexts. As the number of ciphertexts could be huge in a storage system, ciphertext delegation could pose a huge computation overhead to the server.Motivated by the popularity of commodity Trusted Execution Environment (TEE) technologies, this paper initiates the study on hardware-based revocable ABE (HR-ABE) to eliminate the (unscalable) ciphertext delegation and prevent collusion attacks between an untrusted storage server and revoked users. We formalize this new notion and present an efficient HR-ABE construction that also supports outsourced decryption for resource-constrained data users. Furthermore, HR-ABE is also designed to address the potential secret leakage problem suffered by TEE (e.g., due to side-channel attacks) so that the leakage of secrets possessed by TEE does not lead to leakage of user data. We prove HR-ABE’s security formally and benchmark its performance experimentally.

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.

Privacy-preserving data aggregation in mobile crowdsensing (MCS) focuses on mining information from massive sensing data while protecting users' privacy. The existence of multiple concurrent tasks is common in urban environments, so privacy-preserving multi-task data aggregation is essential and useful to a large-scale crowdsensing server. However, existing privacy-preserving data aggregation schemes in MCS mainly focus on the single-task data aggregation and the privacy protection of user's data. Little attention is paid to the privacy of user's decision of accepting tasks. Therefore, we propose a privacy-preserving and server-oriented efficient multi-task data aggregation scheme for MCS based fog computing. The proposed scheme can aggregate multiple concurrent tasks from multiple requesters (e.g., for 9 tasks, the proposed scheme completes all tasks in one round as opposed to existing schemes, which finish 9 tasks in nine rounds). Our scheme protects the privacy of user's decision, user's data, and aggregation result of each requester under collusion attacks. Through formal security analyses, our scheme is proved to be secure and privacy-preserving. Both theoretical analyses and experiments show our scheme is efficient.

Time series data analytics technology significantly benefits modern scientific research, especially in fields such as medical health, financial investment, and transportation. Unfortunately, privacy issues hinder people from handing over the data to a third party for various analytical tasks; because the data may reveal much more individual sensitive information, e.g., disease information from medical data, investment tendency from financial data, or the daily trajectory from transportation data. To break down this barrier, secure computation approaches have shown their importance in processing sensitive data, and have attracted much attention from the industry and research communities. However, when considering the case of secure time-series data analytics (e.g., DTW similarity), we are still far from achieving high efficiency due to high round complexity in communication or expensive computational complexity. We observe that DTW involves a lot of comparison operations and existing approaches in dealing with the comparison require higher communication costs. To this end, this paper studies secure DTW-based analytics with practical efficiency over time series data. Specifically, we propose the framework of secure time series data analytics (STDA) and formulate the problem of top-$k$query for outsourced time series data. Based on threshold Paillier encryption, we present a top-$k$query protocol utilizing the DTW distance as a metric and its security analysis, optimizations, and performance evaluation. The experimental results demonstrate that in a wide-area network with a 10 ms latency, our top-$k$approach outperforms the state-of-the-art by 3x times, while DTW calculation outperforms by 9x times. Correspondingly, the optimized$\mathcal {F}_{\text {DTW}}$achieves 17x times better, and optimized top-$k$achieves 4-10x times better.

Federated learning (FL) is an emerging paradigm for privacy-preserving machine learning, in which multiple clients collaborate to generate a global model through training individual models with local data. However, FL is vulnerable to model poisoning attacks (MPAs) as malicious clients are able to destroy the global model by modifying local models. Although numerous model poisoning defense methods are extensively studied, they remain vulnerable to newly proposed optimized MPAs and are constrained by the necessity to presume a certain proportion of malicious clients. To this end, in this paper, we propose MODEL, a model poisoning defense framework for FL through truth discovery (TD). A distinctive aspect of MODEL is its ability to effectively prevent both optimized and byzantine MPAs. Furthermore, it requires no presupposed threshold for different settings of malicious clients (e.g., less than 33% or no more than 50%). Specifically, a TD-based metric and a clustering-based filtering mechanism are proposed to evaluate local models and avoid presupposing a threshold. Furthermore, MODEL is effective for non-independent and identically distributed (non-IID) training data. In addition, inspired by game theory, we incorporate a truthful and fair incentive mechanism in MODEL to encourage active client participation while mitigating the potential desire for attacks from malicious clients. Extensively comparative experiments demonstrate that MODEL effectively safeguards against optimized MPAs and outperforms the state-of-the-art.

Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following a twin-server architecture based on partially homomorphic cryptosystems have received increasing attention. The Secure Outsourced Computation on Integers (SOCI) toolkit is the state-of-the-art among these schemes which can perform secure computation on integers without requiring the costly bootstrapping operation as in fully homomorphic encryption; however, SOCI suffers from relatively large computation and communication overhead. In this paper, we propose SOCI+ which significantly improves the performance of SOCI. Specifically, SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive, and supports a suite of efficient secure arithmetic computation on integers protocols, including a secure multiplication protocol (SMUL), a secure comparison protocol (SCMP), a secure sign bit-acquisition protocol (SSBA), and a secure division protocol (SDIV), all based on the (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption. In addition, SOCI+ incorporates an offline and online computation mechanism to further optimize its performance. We perform rigorous theoretical analysis to prove the correctness and security of SOCI+. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.3 times more efficient in online runtime and 40% less in communication overheads.

Assigning sensing tasks to appropriate task participants is critical for mobile crowdsensing (MCS) and is an essential optimization problem. However, existing task assignment (or say participant selection) solutions for MCS generally support a single-objective optimization (e.g., minimizing travel distance or maximizing social welfare). Additionally, task assignment for MCS usually requires task participants’ and a task requester's location information, which compromises their location privacy and hinders participation willingness. To achieve task assignment with bi-objective optimization and safeguard bilateral privacy, in this paper, we propose RATE, a privacy-preserving task assignment with bi-objective optimization for MCS. RATE features the following characteristics. First, RATE enables task assignment with bi-objective optimization including maximizing the social welfare and the requester's revenue, simultaneously. Second, RATE achieves bilateral privacy-preserving task assignment with bi-objective optimization by carefully designing underlyingly secure computing protocols. Third, RATE approximates optimal results of task assignments without sacrificing privacy. Theoretical analyses show that RATE protects the location privacy of both the task requester and the task participants. Meanwhile, experimental evaluations demonstrate that RATE outperforms traditional task assignment solutions and generates the task assignment result effectively and efficiently.

Crowdsourcing utilizes the crowd intelligence for pervasive data sensing and processing. When the processing task is a decision-making and optimization problem, the objective is evaluated based on sensed data, which is defined as crowdsourcing-based distributed optimization (CrowdDO). As evolutionary computation (EC) is a powerful technique for black-box and data-driven optimization problems, this paper combines crowdsourcing and EC to propose crowdsourcing-based EC (CrowdEC) for CrowdDO. CrowdEC performs optimization based on a server and a crowd of workers. Once receiving a CrowdDO request, the server posts the problem to workers. Each worker senses its own data and makes local decisions by local EC optimizer. Due to the heterogeneity of worker behaviors and devices, the sensed data are partial with noises, and thus the server needs to coordinate global optimization based on workers information. To avoid the leakage of worker privacy, workers only compare optimization results with adjacent workers and report comparison results to the server. With partial comparison results, the server adopts the competitive ranking to guide workers cooperation and develop the reliability detection to distinguish unreliable workers. A crowdsourcing-based level-based learning swarm optimizer is implemented as an example. Comparison experiments on benchmark testsuites and distributed clustering optimization demonstrate the potential applications of CrowdEC.

Centralized particle swarm optimization (PSO) does not fully exploit the potential of distributed or parallel computing and suffers from single-point-of-failure. Particularly, each particle in PSO comprises a potential solution (e.g., traveling route and neural network model parameters) which is essentially viewed as private data. Unfortunately, previously neither centralized nor distributed PSO algorithms fail to protect privacy effectively. Inspired by secure multiparty computation and multiagent system, this article proposes a privacy-preserving multiagent PSO algorithm (called PriMPSO) to protect each particle's data and enable private data sharing in a privacy-preserving manner. The goal of PriMPSO is to protect each particle's data in a distributed computing paradigm via existing PSO algorithms with competitive performance. Specifically, each particle is executed by an independent agent with its own data, and all agents jointly perform global optimization without sacrificing any particle's data. Thorough investigations show that selecting an exemplar from all particles and updating particles through the exemplar are critical operations for PSO algorithms. To this end, this article designs a privacy-preserving exemplar selection algorithm and a privacy-preserving triple computation protocol to select exemplars and update particles, respectively. Strict privacy analyses and extensive experiments on a benchmark and a realistic task confirm that PriMPSO not only protects particles' privacy but also has uniform convergence performance with the existing PSO algorithm in approximating an optimal solution.

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWDFA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution encompassing data aggregation, incentive design, and privacy protection. Specifically, inspired by FA, CRWODFA initiates an MCS computing paradigm that enables data aggregation and incentive design. Participants can perform aggregation operations on their local data, facilitated by CROWDFA, which supports various common data aggregation operations and bidding incentives. To address privacy concerns, CROWDFA relies solely on an efficient cryptographic primitive known as additive secret sharing to simultaneously achieve privacy-preserving data aggregation and privacy-preserving incentive. To instantiate CROWDFA, this paper presents a privacy-preserving data aggregation scheme (PRADA) based on CROWDFA, capable of supporting a range of data aggregation operations. Additionally, a CROWDFA-based privacy-preserving incentive mechanism (PRAED) is designed to ensure truthful and fair incentives for each participant, while maximizing their individual rewards. Theoretical analysis and experimental evaluations demonstrate that CROWDFA protects participants’ data and bid privacy while effectively aggregating sensing data. Notably, CROWDFA outperforms state-of-the-art approaches by achieving up to 22 times faster computation time.

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good scalability and low deployment cost but raises privacy concerns. In this paper, we propose a privacy-preserving MCS system calledCrowdFLby seamlessly integrating federated learning (FL) into MCS. At a high level, in order to protect participants’ privacy and fully explore participants’ computing power, participants inCrowdFLlocally process sensing data via FL paradigm and only upload encrypted training models to the server. To this end, we design a secure aggregation algorithm (SecAgg) through the threshold Paillier cryptosystem to aggregate training models in an encrypted form. Also, to stimulate participation, we present a hybrid incentive mechanism combining the reverse Vickrey auction and posted pricing mechanism, which is proved to be truthful and fail. Results of theoretical analysis and experimental evaluation on a practical MCS scenario (human activity recognition) show thatCrowdFLis effective in protecting participants’ privacy and is efficient in operations. In contrast to existing solutions,CrowdFLis 3× faster in model decryption and improves an order of magnitude in model aggregation.

Person re-identification (Person Re-ID) is widely regarded as a promising technique to identify a target person through surveillance cameras in the wild. Nevertheless, person Re-ID leads to severe personal image privacy concerns as personal images are stipulated by laws and guidelines as private data. To address these concerns, this article explores the first solution for building a privacy-preserving person Re-ID system. Specifically, this article formulizes privacy-preserving person Re-ID as similarity metrics of encrypted feature vectors because the underlying operation of person Re-ID is to compute the similarity of feature vectors that are extracted from person images by a machine learning model. However, feature vectors are generally denoted by floating-point numbers. To this end, this article exploits a series of new encoding mechanisms and secure batch computing protocols to encrypt floating-point feature vectors and achieve the underlying operation of person Re-ID. Rigorous theoretical analyses demonstrate that this work achieves person Re-ID without compromising any personal image privacy. Furthermore, the proposed secure batch protocols significantly enhance the performance of privacy-preserving person Re-ID while outputting the same precision as the previous method.

In mobile crowdsensing (MCS), sensing data uploaded by dishonest workers may be false or even malicious. Thus, a reputation management system is often set up by using workers’ historical behaviors to indicate the quality of sensing data. As existing management schemes usually protect the reputation update process, reputation scores are generally stored in plaintext, which may destroy the fair bidding property of an MCS system. To address this issue, we propose an anonymous reputation management system based on the dual blockchain architecture, where reputation scores are masked. More precisely, one chain is used to store and update reputation scores, and another chain is responsible for publishing tasks and storing task-related data. To anonymously update and verify the reputation scores without affecting their usages in data sensing process, a kind of ring signature and Pedersen commitment is employed in smart contracts. In addition, a Schnorr signature is generated to make the reputation scores verifiable in the MCS system. We implement a prototype system on Hyperledger Fabric, and simulation results are provided for comparisons with two existing schemes.

In mobile crowdsensing (MCS), providing appropriate rewards is a common and efficient way to motivate participants to participate in sensing tasks. However, the privacy of task participants is not protected well in most quality-aware incentive schemes. Moreover, these schemes are designed for general MCS application scenarios where data are collected by internal sensors embedded in participants’ smartphones, and not suitable for scenarios where additional sensors (ASs) except internal sensors are to collect data (e.g., household medical devices). In scenarios with ASs, malicious participants can fabricate sensing data instead of collecting data from ASs, i.e., the source reliability of sensing data cannot be ensured. To address these issues, we propose P2SIM, a privacy-preserving and the source-reliable incentive mechanism scheme for MCS with ASs. We combine redactable signature with private hash function to achieve the source reliability verification of sensing data without revealing the privacy of participants. Moreover, rewards are divided into two parts: 1) fixed rewards and 2) floating rewards, to enhance the flexibility of rewards distribution. Both formal theoretical analysis and extensive experimental evaluations on a real data set show that the proposed P2SIM is secure and efficient.

Secure outsourced computation is a key technique for protecting data security and privacy in the cloud. Although fully homomorphic encryption (FHE) enables computations over encrypted data, it suffers from high computation costs in order to support an unlimited number of arithmetic operations. Recently, secure computations based on interactions of multiple computation servers and partially homomorphic encryption (PHE) were proposed in the literature, which enable an unbound number of addition and multiplication operations on encrypted data more efficiently than FHE and do not add any noise to encrypted data; however, these existing solutions are either limited in functionalities (e.g., computation on natural numbers only) or leak information of the underlying data. To tackle these shortcomings, this paper proposes Secure Outsourced Computation on Integers (SOCI) based on PHE and a twin-server architecture. Compared with the existing solutions, SOCI supports computations on encrypted integers (vs. natural numbers) and greatly improves the security and correctness of the computations. Results of theoretical analysis and experimental evaluation show that SOCI outperforms existing solutions in computation and communication efficiencies.

Crowdsensing is regarded as a critical component of the Internet of Things (IoT) and has been widely applied in smart city services. Incentive mechanism design, data reliability evaluation, and privacy preservation are the research focuses of crowdsensing. However, most existing incentive mechanisms fail to protect data privacy and evaluate data credibility, simultaneously. Moreover, traditional privacy and reliability-aware incentive schemes are usually challenging to realize real-time reward distribution. To this end, we first point out a single-time slice of failure problem in real-time incentive mechanisms and propose a two-layer truth discovery model (TLTD) to resolve this problem. Then, a reliability-aware real-time incentive mechanism (RRIM) is designed based on the proposed TLTD. In order to evaluate data reliability in a privacy-preserving manner, we build a privacy-preserving truth discovery solution (PriTD) based on secure computation protocols. Finally, our proposed system [privacy and reliability-aware real-time incentive system for crowdsensing (PRICE)] integrating the aforementioned protocols realizes real-time reward distribution, data reliability evaluation, and privacy protection, simultaneously. Theoretical analysis and experimental evaluations on a synthetic and real-world data set demonstrate the feasibility and efficiency of the proposed PRICE.

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to motivate the participation of task participants. However, a monetary incentive mechanism is generally challenging to prevent malicious task participants and a dishonest task requester. Moreover, prior quality-aware incentive schemes are usually failed to preserve the privacy of task participants. Meanwhile, most existing privacy-preserving incentive schemes ignore the data quality of task participants. To tackle these issues, we propose a privacy-preserving and data quality-aware incentive scheme, called PACE. In particular, data quality consists of the reliability and deviation of data. Specifically, we first propose a zero-knowledge model of data reliability estimation that can protect data privacy while assessing data reliability. Then, we quantify the data quality based on the deviation between reliable data and the ground truth. Finally, we distribute monetary rewards to task participants according to their data quality. To demonstrate the effectiveness and efficiency of PACE, we evaluate it in a real-world dataset. The evaluation and analysis results show that PACE can prevent malicious behaviors of task participants and a task requester, and achieves both privacy-preserving and data quality measurement of task participants.

The minimum travel distance of task participants is one of the significant optimization objectives of privacy-preserving task assignment in mobile crowdsensing (MCS). However, when the travel distance is minimized, most of the previous schemes only focus on the task participant privacy and disregard the task requester privacy. Moreover, existing solutions usually only support the constraint of a single type, such as equality constraints or range constraints. In this paper, we propose a bilateral privacy-preserving Task Assignment mechanism for MCS (iTAM), which protects not only the task participants privacy but also the task requesters privacy and can minimize the travel distance. Furthermore, iTAM provides both equality and range constraints of task assignment by utilizing the Paillier cryptosystem. To accommodate the multiple relations between the task participants and the task, we propose the single/multiple task participants selection problems for a task requiring task participants to compete and cooperate. Experimental evaluations over synthetic and real-world data illustrate that iTAM is feasible and effective. Compared with the state-of-the-art, iTAM positively solves the optimal problem of travel distance. The complexities of iTAM are$\mathcal {O}(n)$and$\mathcal {O}(n\log n)$for a single and multiple task participants selection problems, respectively.

A reliable mobile crowdsensing (MCS) application usually relies on sufficient participants and trustworthy data. However, privacy concerns reduce participants' willingness to participate in sensing tasks. The uncertainty of participant behavior and heterogeneity of sensing devices result in the unreliability of sensing data and further bring unreliable MCS services. Hence, it is crucial to estimate the reliability of sensing data and protect privacy. Unfortunately, most existing privacy-preserving data estimation solutions are designed for single-type data. In practice, however, heterogeneous sensing data are ubiquitous in data integration tasks. To this end, we propose a privacy-preserving reliability estimation solution of heterogeneous data for MCS, called IronM, which is effective for text, number, and multimedia data (e.g., image, audio, and video). Specifically, IronM first formulates the reliability assessment of text, number, and multimedia data as equality and range constraints, and then estimates the reliability of heterogeneous data through our proposed privacy-preserving hybrid constraints assessment mechanism. Privacy analysis demonstrates that IronM can not only evaluate the reliability of heterogeneous data but also protect data confidentiality. The experimental results in real-world datasets show the effectiveness and efficiency of IronM.