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To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers' data are computed and transmitted without complicate encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers' perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads.

Searchable symmetric encryption (SSE) enables a user to outsource a private dataset to a cloud server in encrypted form while retaining the ability to search over the encrypted outsourced data. The existing SSE schemes improve the search and safety performances from different perspectives. However, almost none of the existing SSE schemes considers the data distribution issues. We find that when the dataset is not distributed uniformly, the search quality based on the conventional methods decreases. Therefore, the existing SSE schemes cannot guarantee high search quality when faced with non-uniform datasets. In addition, most existing SSE solutions cannot hide the distribution of the query set. In this paper, we design a Secure similarity search over Encrypted Non-uniform and high-dimensional Datasets (SEND) with a novel way to enhance security. The basic idea is to combine SSE with locality-sensitive hashing (LSH). Unlike earlier schemes, SEND uses selective hashing, which has better performance for non-uniform datasets. Also, we present a novel approach to hide the distribution of the query set, which makes SEND more secure. Our experimental results indicate SEND achieves a high search quality of recall and precision, and it is proven secure against adaptively chosen query attacks in the standard model.

With the explosive growth of data volume in the cloud computing environment, data owners are increasingly inclined to store their data on the cloud. Although data outsourcing reduces computation and storage costs for them, it inevitably brings new security and privacy concerns, as the data owners lose direct control of sensitive data. Meanwhile, most of the existing ranked keyword search schemes mainly focus on enriching search efficiency or functionality, but lack of providing efficient access control and formal security analysis simultaneously. To address these limitations, in this paper we propose an efficient and privacy-preserving Multi-keyword Ranked Search scheme with Fine-grained access control (MRSF). MRSF can realize highly accurate ciphertext retrieval by combining coordinate matching with Term Frequency-Inverse Document Frequency (TF-IDF) and improving the secure kNN method. Besides, it can effectively refine users' search privileges by utilizing the polynomial-based access strategy. Formal security analysis shows that MRSF is secure in terms of confidentiality of outsourced data and the privacy of index and tokens. Extensive experiments further show that, compared with existing schemes, MRSF achieves higher search accuracy and more functionalities efficiently.

Internet of Things (IoT) devices and systems are becoming increasingly commonplace, and as such systems scale up, so do the computational and storage requirements. Hence, one recent trend is to outsource data from IoT devices to remote systems. To facilitate both ciphertext retrieval and data confidentiality in the outsourced data, a number of searchable encryption approaches have been proposed in the literature. However, due to limited keyword space, a number of searchable encryption schemes are vulnerable to keyword guessing attacks (KGA). In addition, existing searchable encryption approaches generally do not consider the hierarchical structure in which users at different levels require varying access privileges. Furthermore, existing searchable encryption schemes seldom provide time-controlled access control. Therefore, in this paper we propose a time-controlled hierarchical multi-keyword search by using a double-server architecture to mitigate KGA. In our approach, we also build a public key tree to support different access permissions for hierarchical users. Formal security analysis shows that our scheme is secure, and extensive experiments demonstrate that our scheme is practical.

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand the inside KGA and achieve verifiable searchability. Based on the basic VSEF, we then present the enhanced VSEF to support multi-keyword search, multi-key encryption and dynamic updates (e.g., data modification, data insertion, and data deletion) at the same time, which highlights the importance of practicability and scalability of SE in real-world application scenarios. We conduct extensive experiments using the Enron email dataset to demonstrate that the enhanced VSEF achieves high efficiency while resisting to the inside KGA and supporting the verifiability of search results.

As a novel network infrastructure that realizes the interconnection of humans, machines, and things, the 5G-based or blockchain-based applications have been widely deployed in the Industrial Internet of Things (IIoT). However, there are still many challenges to be solved, such as poor scalability, low efficiency, and privacy leakages. In the special issue, we present eight advanced solutions in data analysis, secure communication, authentication, access control, and data deduplication.

With the emergence of intelligent terminals, the Content-Based Image Retrieval (CBIR) technique has attracted much attention from many areas (i.e., cloud computing, social networking services, etc.). Although existing privacy-preserving CBIR schemes can guarantee image privacy while supporting image retrieval, these schemes still have inherent defects (i.e., low search accuracy, low search efficiency, key leakage, etc.). To address these challenging issues, in this paper we provide a similarity Search for Encrypted Images in secure cloud computing (called SEI). First, the feature descriptors extracted by the Convolutional Neural Network (CNN) model are used to improve search accuracy. Next, an encrypted hierarchical index tree by using K-means clustering based on Affinity Propagation (AP) clustering is devised, which can improve search efficiency. Then, a limited key-leakage k-Nearest Neighbor (kNN) algorithm is proposed to protect key from being completely leaked to untrusted image users. Finally, SEI is extended to further prevent image users' search information from being exposed to the cloud server. Our formal security analysis proves that SEI can protect image privacy as well as key privacy. Our empirical experiments using a real-world dataset illustrate the higher search accuracy and efficiency of SEI.

Anomaly detection offers a powerful approach to identifying unusual activities and uncommon behaviors in real-world video scenes. At present, convolutional neural networks (CNN) have been widely used to tackle anomalous events detection, which mainly rely on its stronger ability of feature representation than traditional hand-crafted features. However, massive video data and high cost of CNN model training are a challenge to achieve satisfactory detection results for resource-limited users. In this paper, we propose a secure video anomaly detection framework (SecureAD) based on CNN. Specifically, we introduce additive secret sharing to design several calculation protocols for achieving safe CNN training and video anomaly detection. Besides, we propose a Bloom filter based fine-grained access control policy to authenticate legitimate users, without leaking the privacy of raw personal attributes. In addition, edge computing instead of cloud computing is integrated into the architecture to reduce response time between servers and users in an outsourced environment. Finally, we prove that the proposed SecureAD achieves secure video anomaly detection without compromising the privacy of the related data. Also, the simulation results demonstrate the effectiveness and security of our SecureAD.

As an emerging concept in the intelligent transportation system (ITS), Opportunistic Autonomous Vehicle Platoon (OAVP) enables autonomous vehicles to self-organize a temporary platoon and travel together. Whereas, electing the platoon leader is a crucial issue to be solved in this scenario. The platoon leader not only suffers from more wind resistance but also spends more computation resources in dealing with tremendous information obtained from the surrounding environment. Hence, vehicles tend to be a follower rather than a leader. In this paper, we propose a reputation-based leader election system for OAVP named RLE, which contains two sub-systems: leader election and incentive mechanism. In the former one, a reputation-based election scheme is first constructed to elect a relatively trustful leader according to the reputation value recorded on the blockchain. The proposed scheme integrates previous experience with recommendations from other members. In the second sub-system, an incentive mechanism is designed to stimulate platoon members to participate actively in the process of election, which is based on the recorded real-time fuel economy among vehicles participating in a platoon. Security analysis shows that the system is sufficient to deal with potential security threats. Experimental results based on the simulated platform demonstrate the practicality and feasibility of our solution.

Vehicle platoon, a novel vehicle driving paradigm that organizes a group of vehicles in the nose-to-tail structure, has been considered as a potential solution to reduce traffic congestion and increase travel comfort. In such a platoon system, head vehicles' performances are usually evaluated by user vehicles' feedbacks. Selection of an appropriate and reliable head vehicle while not disclosing user vehicles' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving platoon recommendation scheme, called TPPR, to enable potential user vehicles avoid selecting the malicious head vehicles. The basic concept of TPPR is that each user vehicle holds a trust value, and the reputation score of the head vehicle is calculated via a truth discovery process. To preserve vehicles' privacy, pseudonyms and Paillier cryptosystem are applied. In addition, novel authentication protocols are designed to ensure that only the valid vehicles (i.e., the vehicles holding the truthful trust values and joining the vehicle platoon) can pass the authentication. A comprehensive security analysis is conducted to prove that the proposed TPPR scheme is secure against several sophisticated attacks in vehicular ad hoc networks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.

Healthcare Internet-of-Things (IoT) is an emerging paradigm that enables embedded devices to monitor patients' vital signals and allows these data to be aggregated and outsourced to the cloud. The cloud enables authorized users to store and share data to enjoy on-demand services. Nevertheless, it also causes many security concerns because of the untrusted network environment, dishonest cloud service providers and resource-limited devices. To preserve patients' privacy, existing solutions usually apply cryptographic tools to offer access controls. However, fine-grained access control among authorized users is still a challenge, especially for lightweight and resource-limited end-devices. In this paper, we propose a novel healthcare IoT system fusing advantages of attribute-based encryption, cloud and edge computing, which provides an efficient, flexible, secure fine-grained access control mechanism with data verification in healthcare IoT network without any secure channel and enables data users to enjoy the lightweight decryption. We also define the formal security models and present security proofs for our proposed scheme. The extensive comparison and experimental simulation demonstrate that our scheme has better performance than existing solutions.

In the era of cloud computing, to achieve convenient location-based service (LBS), consumers such as users, companies, and organizations prefer subcontracting massive geographical data to public clouds after encryption for privacy and security. However, numerous harmful cyber-attacks happen on those public clouds in an unpredicted and hourly manner. To alleviate those concerns, various secure query schemes on the encrypted data have been proposed in the literature. As a fundamental query of LBSs, forward-secure range query has not been well investigated. To address this issue, we propose a lightweight and forward-secure range query (LS-RQ) on geographically encrypted data, which soundly balances between security and efficiency. Promisingly, we design an index mechanism to manage geographical data on the public clouds, while not compromising the privacy of data. Moreover, our LS-RQ schemes provide a convenient approach to range query on geographically encrypted data on-the-fly. We also rigorously prove that LS-RQ is forward-secure. Finally, extensive experimental studies are performed on both real and synthetic datasets. By observation, our LS-RQ schemes are highly efficient in realistic environments. Particularly, on encrypted datasets with about 1, 000, 000 geographical data, our solution to secure range query takes strictly less than a second.

With the advancement of mobile internet, cloud computing, and smart sensing devices, Location-Based Services (LBSs) have become more and more indispensable in the Internet of Things (IoT) based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This paper proposes two Secure Dynamic Spatial Keyword Queries (SDSKQ) constructions that support expressive query types and dynamic update. Firstly, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure for spatio-textual data, named Encrypted Textual Signature Quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world datasets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.

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 (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 dataset demonstrate the feasibility and efficiency of the proposed PRICE.

For group signatures (GS) supporting membership revocation, verifier-local revocation (VLR) mechanism is the most flexible choice. As a post-quantum secure cryptographic counterpart of classical schemes, the first dynamic GS-VLR scheme over lattices was put forward by Langlois et al. at PKC 2014; furthermore, a corrected version was shown at TCS 2018. However, both designs are within Bonsai trees and featuring bit-sizes of group public-key and member secret signing key proportional to log N where N is the group size; therefore, both schemes are not suitable for a large group. In this paper, we provide an improved dynamic GS-VLR over lattices, which is efficient by eliminating a O log N factor for both sizes. To realize the goal, we adopt a more efficient and compact identity-encoding technique. At the heart of our new construction is a new Stern-type statistical zero-knowledge argument of knowledge protocol which may be of some independent cryptographic interest.

Dynamic Searchable Symmetric Encryption (DSSE) has gained increasing popularity as it enables users to perform both file updates and ciphertext retrieval over encrypted data . However, existing DSSE schemes still lead to privacy leakage ( e.g., forward and backward privacy) in the dynamic setting. Some forward and backward secure DSSE schemes have been proposed, but still cannot support the keyword shielding flexibly. To solve this challenging issue, we propose a Forward and Backward Authorized Keyword Search (FB-AKS) scheme with recoverable keyword shielding by using trapdoor permutations and puncturable encryption in this paper. Compared with existing forward and backward private schemes, FB-AKS achieves keyword authorization flexibly ( e.g., keyword shielding, keyword un-shielding). The formal security analysis proves that FB-AKS achieves forward and backward security. And extensive experiments demonstrate that FB-AKS has less computation and storage overheads .

A robust network intrusion detection system (NIDS) plays an important role in cyberspace security for protecting confidential systems from potential threats. In real world network, there exists complex correlations among the various types of network traffic information, which may be respectively attributed to different abnormal behaviors and should be make full utilized in NIDS. Regarding complex network traffic information, traditional learning based abnormal behavior detection methods can hardly meet the requirements of the real world network environment. Existing methods have not taken into account the impact of various modalities of data, and the mutual support among different data features. To address the concerns, this paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively. In order to accurately explore the connotation of traffic information, multiple basic feature datasets are established considering different aspects of traffic information such as time, space, and load. Then, considering the association and correlation among the basic feature datasets, multiple comprehensive feature datasets are set up to meet the requirements of real world abnormal behavior detection. In specific, stacking ensemble learning is conducted on multiple comprehensive feature datasets, and thus an effective multi-dimensional global anomaly detection model is accomplished. The experimental results on the dataset KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017 have shown that MFFSEM significantly outperforms the basic and meta classifiers adopted in our method. Furthermore, its detection performance is superior to other well-known ensemble approaches.

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prevalent privacy model for LBSs. Although Geo-Ind guarantees the location privacy, users’ other privacy concerns are still at risk if the location perturbation behavior is exposed due to implausible reported locations. Through experiments we find the probability that the classical Geo-Ind mechanism perturbs the true location to implausible areas can be more than 50%. To address it, we first propose an enhanced privacy definition beyond Geo-Ind, called Perturbation-Hidden, to prevent location perturbation behaviors of users from being recognized by guaranteeing their pseudo-locations plausible. Then we design a mechanism to achieve this definition by transplanting the differential private exponential mechanism to our approach. Furthermore, we propose a method for determining the retrieval area utilizing dynamic programming to ensure the accuracy of LBSs. Finally, we theoretically prove that our mechanism satisfies the privacy definition. Extensive experiments on simulations and a real-world dataset show that our proposal achieves 100% plausible pseudo-locations while ensuring high query precision and recall.

As a milestone in the development of outsourcing services, cloud computing enables an increasing number of individuals and enterprises to enjoy the most advanced services from outsourcing service providers. Because online payment and data security issues are involved in outsourcing services, the mutual distrust between users and service providers may severely impede the wide adoption of cloud computing. Nevertheless, most existing solutions only consider a specific type of services and rely on a trusted third-party to realize fair payment. In this paper, to realize secure and fair payment of outsourcing services in general without relying on any third-party, trusted or not, we introduce BPay, an outsourcing service fair payment framework based on blockchain in cloud computing. We first propose the system architecture, adversary model and design goals of BPay, then describe the design details. Our security and compatibility analysis indicates that BPay achieves soundness and robust fairness and it is compatible with the Bitcoin blockchain and the Ethereum blockchain. The key to the robust fairness and compatibility lies in an all-or-nothing checking-proof protocol and a top-down checking method. In addition, our experimental results show that BPay is computationally efficient. Finally, we present the applications of BPay in outsourcing services.

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.

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi-owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. In addition, due to privacy concerns on access policies, most existing schemes are vulnerable to off-line keyword-guessing attacks if the keyword space is of polynomial size. Furthermore, it is difficult to identify malicious users who leak the secret keys when more than one data user has the same subset of attributes. In this paper, we present a privacy-preserving CP-ABKS system with hidden access policy in Shared Multi-owner setting (basic ABKS-SM system), and demonstrate how it is improved to support malicious user tracing (modified ABKS-SM system). We then prove that the proposed ABKS-SM systems achieve selective security and resist off-line keyword-guessing attack in the generic bilinear group model. We also evaluate their performance using real-world datasets.

With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task’s location policy privacy and providing multi-level privacy preservation for workers’ locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task’s location policy. To achieve multi-level privacy preservation for workers’ driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice.

In this paper, we propose a privacy-preserving reinforcement learning framework for a patient-centric dynamic treatment regime, which we refer to as Preyer. Using Preyer, a patient-centric treatment strategy can be made spontaneously while preserving the privacy of the patient's current health state and the treatment decision. Specifically, we first design a new storage and computation method to support noninteger processing for multiple encrypted domains. A new secure plaintext length control protocol is also proposed to avoid plaintext overflow after executing secure computation repeatedly. Moreover, we design a new privacy-preserving reinforcement learning framework with experience replay to build the model for secure dynamic treatment policymaking. Furthermore, we prove that Preyer facilitates patient dynamic treatment policymaking without leaking sensitive information to unauthorized parties. We also demonstrate the utility and efficiency of Preyer using simulations and analysis.

Private set intersection (PSI) is a fundamental cryptographic protocol which has a wide range of applications. It enables two clients to compute the intersection of their private datasets without revealing non-matching elements. The advent of cloud computing drives the ambition to reduce computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, some cryptographic methods should be applied to maintain the confidentiality of datasets. But, in doing so, data owners may be excluded from access control on their outsourced datasets. Therefore, to control access rights and to interact with authorized users, they have to be online during the protocol. On the other hand, none of the existing cloud-based PSI schemes support fine-grained access control over outsourced datasets. This paper, for the first time, proposes an attribute-based private set intersection (AB-PSI) scheme providing fine-grained access control. AB-PSI allows a data owner to control intersection computations on its outsourced dataset by defining an access control policy. We also provide security definitions for an AB-PSI scheme and prove the security of our scheme in the standard model. We implement our scheme and report performance evaluation results.

Resource-constrained users outsource the massive image data to the cloud to reduce storage and computation overhead locally, but security and privacy concerns seriously hinder the applications of outsourced image processing services. Besides, existing image processing solutions in the encrypted domain still bring high computation overhead, and even lead to characteristic loss. To this end, we propose a Privacy-Preserving Krawtchouk Moment (PPKM) feature extraction framework over encrypted image data by utilizing the Paillier cryptosystem. First, a mathematical framework for PPKM implementation and image reconstruction is presented in the encrypted domain. Then, the detailed expanding factor and upper bound analysis shows that plaintext Krawtchouk moment and plaintext image reconstruction can be realized over encrypted image with PPKM. Furthermore, the computation complexity of PPKM can be significantly reduced with the block-based parallel algorithm. Experimental results verify that the PPKM is feasible and acceptable in practice in terms of image reconstruction capability and image recognition accuracy.

Traitor tracing scheme is a paradigm to classify the users who illegal use of their decryption keys in cryptosystems. In the ciphertext-policy attribute-based cryptosystem, the decryption key usually contains the users’ attributes, while the real identities are hidden. The decryption key with hidden identities enables malicious users to intentionally leak decryption keys or embed the decryption keys in the decryption device to gain illegal profits with a little risk of being discovered. To mitigate this problem, the concept of blackbox traceability in the ciphertext-policy attribute-based scheme was proposed to identify the malicious user via observing the I/O streams of the decryption device. However, current solutions with blackbox traceability are impractical since either the composite-order group or the linear complexity of system users is required. In this article, we proposed a secure ciphertext-policy attribute-based set encryption scheme with the short decryption key. The proposed scheme bases on the prime-order group to improve computational performances and aggregates multiple attributes into a constant-size attribute set to reduce the costs of communication overheads. By applying our proposed scheme with fingerprint codes, we then give an instantiation of the ciphertext-policy attribute-based scheme with blackbox traceability. Our scheme is provably secure under various q-type assumptions.

The increasingly sophisticated cyber attacks have become a serious challenge for Industrial Internet of Things (IIoT), which presents two new characteristics: low frequency and multi-stage. That is, hackers could gain authority to attack industrial equipment/infrastructure gradually in a long interval through lurking, lateral intrusion and privilege escalation. While, the existing Machine Learning (ML) based intrusion detection schemes all require the participation of expert knowledge, so it is difficult to adaptively select an attack interval and a retraining period of the detection model in IIoT, resulting in poor detection performance. To solve above problems, a bidirectional long and short-term memory network with multi-feature layer (B-MLSTM) is designed. Firstly, sequence and stage feature layers are introduced in the model training phase model which can learn the corresponding attack interval from historical data, so that the model can effectively detect attacks with different intervals. Then, a double-layer reverse unit is introduced to update the detection model. By collecting information from test data and making association analysis with historical data, the retraining period is adaptively selected to match the new attack interval. Compared with the previous works, our proposed scheme has a lower false positive rate than existing schemes by at least 46.79%, and the false negative rate is reduced by at least 79.85%, which are carried out on three classic IIoT datasets.

With the booming of Internet of Things (IoT), smart health (s-health) is becoming an emerging and attractive paradigm. It can provide an accurate prediction of various diseases and improve the quality of healthcare. Nevertheless, data security and user privacy concerns still remain issues to be addressed. As a high potential and prospective solution to secure IoT-oriented s-health applications, ciphertext policy attribute-based encryption (CP-ABE) schemes raise challenges, such as heavy overhead and attribute privacy of the end users. To resolve these drawbacks, an optimized vector transformation approach is first proposed to efficiently transform the access policy and user attribute set into respective vectors of shorter length while other approaches result in redundant and longer vectors. Our transformation approach can greatly relieve the costly overheard of key generation, encryption, and decryption phases. Then, based on the transformation approach and the offline/online computation technology, we propose a lightweight policy-hiding CP-ABE scheme for the IoT-oriented s-health application. With our proposed scheme, data users in the s-health system can perform lightweight encryption and decryption without leaking any sensitive privacy about the attributes of the user. Finally, the formal security analysis, the theoretic performance evaluation and experiment results indicate that the solution is secure and efficient.

The vehicular ad hoc network (VANET) has been considered as one of the most prominent technologies for improving the efficiency and safety of modern transportation systems. However, VANET will present a unique range of challenges and opportunities for security. In particular, key management, as the footstone to build a practical security framework in VANET, becomes a research hotspot. In this paper, we investigate key management based on blockchain for VANET. We first propose an efficient decentralized key management mechanism for VANET with blockchain (DB-KMM) to automatically realize the registration, update and revocation of user's public key. At the same time, we present a lightweight mutual authentication and key agreement protocol based on the bivariate polynomial. Then, we analyze the security of DB-KMM in the universally composable framework and show that the mechanism can prevent the typical attacks including internal attacks, DoS attacks, public key tampering attacks and collusion attacks. Finally, we analyze the performance of the proposed scheme through experiments and simulation. Experiment results show that DB-KMM has better performance than the existing schemes in terms of communication, storage, computation overhead and latency.

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.

Searchable encryption is an important technique for public cloud storage service to provide user data confidentiality protection and at the same time allow users performing keyword search over their encrypted data. Previous schemes only deal with exact or fuzzy keyword search to correct some spelling errors. In this paper, we propose a new wildcard searchable encryption system to support wildcard keyword queries which has several highly desirable features. First, our system allows multiple keywords search in which any queried keyword may contain zero, one or two wildcards, and a wildcard may appear in any position of a keyword and represent any number of symbols. Second, it supports simultaneous search on multiple data owner's data using only one trapdoor. Third, it provides flexible user authorization and revocation to effectively manage search and decryption privileges. Fourth, it is constructed based on homomorphic encryption rather than Bloom filter and hence completely eliminates the false probability caused by Bloom filter. Finally, it achieves a high level of privacy protection since matching results are unknown to the cloud server in the test phase. The proposed system is thoroughly analyzed and is proved secure. Extensive experimental results indicate that our system is efficient compared with other existing wildcard searchable encryption schemes in the public key setting.

While decision-making task is critical in knowledge transfer, particularly from multi-source domains, existing knowledge transfer approaches are not generally designed to be privacy preserving. This has potential legal and financial implications, particularly in sensitive applications such as financial market forecasting. Therefore, in this paper, we propose a Privacy-preserving Multi-party Knowledge Transfer system (PMKT), based on decision trees, for financial market forecasting. Specifically, in PMKT, we leverage a cryptographic-based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, and design a secure computation mechanism to facilitate privacy-preserving knowledge transfer. An encrypted user-submitted request from the target domain can also be sent to the cloud server for secure prediction. Also, the use of decision trees allows us to provide interpretability of the predictions. We then demonstrate how PMKT can achieve privacy guarantees, and empirically show that PMKT achieves accurate forecasting without compromising on accuracy.

With the promotion of online medical pre-diagnosis system, more and more research has begun to pay attention to the issue of privacy, and existing privacy-preserving schemes are designed for single-label data. However, medical users may infect many different diseases at the same time, it is necessary to take multi-label instances into account. In this paper, we propose an efficient and privacy-preserving multi-level medical pre-diagnosis scheme, called CREDO, which based on multi-label k-nearest-neighbors (ML-kNN). With CREDO, medical users can ensure their sensitive health information secure, and service provider can provide high-efficiency service without revealing pre-diagnosis model data. Specifically, service provider first narrows down the scope of medical instances needed to be calculated based on k-means clustering, then provides service for medical users based on ML-kNN classification. The query vector is encrypted before being sent out and directly operated in the service provider, meanwhile, the pre-diagnosis result can only be achieved by the medical user. Through extensive analysis, we show that CREDO can resist multifarious known security threats, and has much lower computation complexity than the compared scheme. Moreover, performance evaluations based on a real medical dataset demonstrate that our proposed scheme is highly efficient in terms of computation and communication overhead.

In cloud era, it is necessary to store sensitive data in an encrypted form. This arises the interesting and challenging problem of searching on encrypted data. However, previous Public-key Encryption with Keyword Search (PEKS) inherently cannot resist against inside keyword guessing attacks. To alleviate this issue, recently Huang and Li proposed the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS), which requires the data sender not only encrypting a keyword using the receiver’s public key, but also authenticating it using his secret key. This paper first revisits HL-PAEKS security model and finds that it did not capture a realistic threat, called (outside) chosen multi-ciphertext attacks. That is, an outside adversary can decide whether two encrypted files share some identical keywords or not. To resolve this issue, we propose a new PAEKS security model that captures both (outside) chosen multi-ciphertext attacks and (inside) keyword guessing attacks. Then, we give a concrete PAEKS scheme and prove its security in the new PAEKS security model. We also propose a method to simplify data sender’s key management using identity-based key exchange protocol. Finally, we provide implementation results of our schemes to show the comparable efficiency of our schemes with previous PEKS/PAEKS schemes.

Multimedia data, ranging from text, audio, video to animation, undergoes intellectual property protection or has high sensitivity. To deal with privacy leakage during multimedia sharing and dissemination, it is crucial to trace the origin and transformation history of multimedia data, which is called multimedia provenance. In this paper, we construct a multimedia access control system with secure provenance in fog-cloud computing networks, which is designed based on attribute based encryption (ABE) and zero of knowledge technologies. The proposed scheme realizes confidentiality of multimedia big data that is outsourced to the cloud, anonymity of data provider, fine-grained access control of encrypted data, irrefutable of the provenance data, and traceability of data provider. We utilize the fog server to reduce user’s decryption burden and transfer partial decryption tasks. The suggested system is formally proved indistinguishable against chosen plaintext attack (IND-CPA). The simulation and experimental results indicate that our system has low communication overhead and low computation cost.

Mobile health (mHealth) has emerged as a new patient centric model which allows real-time collection of patient data via wearable sensors, aggregation and encryption of these data at mobile devices, and then uploading the encrypted data to the cloud for storage and access by healthcare staff and researchers. However, efficient and scalable sharing of encrypted data has been a very challenging problem. In this paper, we propose a Lightweight Sharable and Traceable (LiST) secure mobile health system in which patient data are encrypted end-to-end from a patient's mobile device to data users. LiST enables efficient keyword search and fine-grained access control of encrypted data, supports tracing of traitors who sell their search and access privileges for monetary gain, and allows on-demand user revocation. LiST is lightweight in the sense that it offloads most of the heavy cryptographic computations to the cloud while only lightweight operations are performed at the end user devices. We formally define the security of LiST and prove that it is secure without random oracle. We also conduct extensive experiments to access the system's performance.

Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform for doing machine learning. However, the issue of data privacy is far from being well solved and thus has been a general concern in the cloud-aided machine learning. In this work, we investigate the study of how to efficiently do decision tree training and evaluation in the cloud and meanwhile achieve privacy preservation. Unlike existing cloud server-assisted model training approaches, in our proposed solution, the whole training process is mostly done by the cloud service provider who owns the machine learning model. Since the cloud cannot directly divide the encrypted dataset according to the best attributes selected, we propose a new method for decision tree training without dataset splitting. Precisely, we design three methods for decision tree training with the different tradeoff between privacy and efficiency. In all of these methods, the outsourced data are not revealed to the cloud service provider. We also propose a privacy-preserving decision tree evaluation scheme where the cloud service provider learns nothing about the user’s input and the classification result while the trained model is kept secret to the user who could only learn the classification result. Compared with previous decision tree evaluation work, our scheme achieves desirable privacy preservation against both the user and the cloud service provider, and also minimizes the user’s computation and communication costs. Moreover, besides protecting the data confidentiality, our proposed scheme also supports off-line users and thus has good scalability. The real-world dataset-based experimental results demonstrate that our system is of desirable utility and efficiency.

Attribute-based keyword search (ABKS), as an important type of searchable encryption, has been widely utilized for secure cloud storage. In a key-policy attribute-based temporary keyword search (KP-ABTKS) scheme, a private key is associated with an access policy that controls the search ability of the user, while a search token is associated with a time interval that controls the search time of the cloud server. However, after a careful study, we uncover that the only existing KP-ABTKS construction [1] is not secure. Through two carefully designed attacks, we first show that the cloud server can search the ciphertext in any time. As a result, their scheme cannot support temporary keyword search. To address this problem, we present an enhanced KP-ABTKS scheme and prove that it is selectively secure against chosen-keyword attack in the random oracle model. The proposed scheme achieves both fine-grained search control and temporary keyword search simultaneously. In addition, the performance evaluation indicates that our scheme is practical.

Nowadays, outsourcing data and machine learning tasks, e.g., $k$ -nearest neighbor (KNN) classification, to clouds has become a scalable and cost-effective way for large scale data storage, management, and processing. However, data security and privacy issue have been a serious concern in outsourcing data to clouds. In this article, we propose a privacy-preserving KNN classification scheme on cloud data in a twin-cloud model based on an additively homomorphic cryptosystem and secret sharing. Compared with existing works, we redesign a set of lightweight building blocks, such as secure square Euclidean distance, secure comparison, secure sorting, secure minimum, and maximum number finding, and secure frequency calculating, which achieve the same security level but with higher efficiency. In our scheme, data owners stay offline, which is different from secure-multiparty computation-based solutions which require data owners’ stay online during computation. In addition, query users do not interact with the cloud except sending query data and receiving the query results. Our security analysis shows that the scheme protects outsourced data security and query privacy, and hides access patterns. The experiments on real-world dataset indicate that our scheme is significantly more efficient than existing schemes.

Searchable encryption is an effective way to ensure the security and availability of encrypted outsourced cloud data. Among existing solutions, the keyword exact search solution is relatively inflexible, while the fuzzy keyword search solution either has a high index overhead or suffers from the false-positive. Furthermore, no existing fuzzy keyword search solution considers the homoglyph search on encrypted data. In this paper, we propose an efficient privacy-preserving homoglyph search scheme supporting arbitrary languages (POSA, in short). We enhance the performance of the fuzzy keyword search in three aspects. Firstly, we formulate the similarity of homoglyph and propose a privacy-preserving homoglyph search. Secondly, we put forward an index build mechanism without the false-positive, which reduces the storage overhead of the index and is suitable for arbitrary languages. Thirdly, POSA returns just the user’s search, i.e., all returned documents contain the search keyword or its homoglyph. The theoretical analysis and experimental evaluations on real-world datasets demonstrate the effectiveness and efficiency of POSA.

In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.

As the next generation of digital microfluidic biochips (DMFBs) platform, Micro-electrode-dot-array (MEDA) has sea-of-micro-electrodes that support fine-grained control of the droplet and constant real-time monitoring of droplet paths. As MEDA biochips are expected to be had a booming market in the near future, their security, especially the protection of intellectual property (IP), has become the focus of the academic community. In this paper, we first propose a logic encryption-based IP protection scheme for MEDA biochips, called MEDASec, which is without modifying the structure. Our scheme aims to hide the droplet mix-split operation in bioassay, making the IP akin to a black-box in the manufacturing process. Besides, increasing computing power has facilitated the threat of brute force attacks on the secret key. In response to the brute force attack, we propose security metrics for the first time, called RRA, to evaluate the ratio of reagents that result from biochemical reactions. Experimental results on multiple bioassays demonstrate the effectiveness of the proposed logical encryption strategy against the brute force attack.

The Internet of Things (IoT)-based health system provides patients and doctors a lot of conveniences, doctors can diagnose patients in time through monitored health data. However, doctors may make a wrong diagnosis of the patient’s illness, if the health data stored on the cloud is tampered or lost due to an external attack or power failure. Therefore, it is of great importance to verify the integrity of the patient’s health data stored on the cloud. Existing data integrity verification technologies usually need data owner to compute data authenticators for verification, but sensors on a patient in IoT-based health system have limited computation ability, thus existing data integrity verification technologies are not very suitable in IoT-based health system. This paper proposes a lightweight secure IoT-based health storage system to solve this problem. The proposed system employs edge server to compute data authenticators and verify data integrity, greatly reduces the system’s computation cost and management burden of third party verifier. Security analysis and performance evaluation show the proposed system is feasible.

Ciphertext-policy attribute-based keyword search (CP-ABKS) schemes facilitate the fine-grained keyword search over encrypted data, such as those sensed/collected from Industrial Internet of Things (IIoT) devices and stored in the cloud. However, existing CP-ABKS schemes generally have significant computation and storage requirements, which are beyond those of resource-constrained IIoT devices. Therefore, in this paper, we design a secure online/offline data sharing framework (DSF), which supports online/offline encryption and outsourced decryption. Using the healthcare setting as a case study, we demonstrate how DSF can be deployed in the cloud-assisted Healthcare IIoT (HealthIIoT) system. We not only prove that the DSF is selectively secure in the chosen access structure security model but also demonstrate its efficiency and feasibility in practical scenarios using experiments.

Training data distributed across multiple different institutions is ubiquitous in disease prediction applications. Data collection may involve multiple data sources who are willing to contribute their datasets to train a more precise classifier with a larger training set. Nevertheless, integrating multiple-source datasets will leak sensitive information to untrusted data sources. Hence, it is imperative to protect multiple-source data privacy during the predictor construction process. Besides, since disease diagnosis is strongly associated with health and life, it is vital to guarantee prediction accuracy. In this paper, we propose a privacy-preserving and high-accurate outsourced disease predictor on random forest, called PHPR . PHPR system can perform secure training with medical information which belongs to different data owners, and make accurate prediction. Besides, the original data and computed results in the rational field can be securely processed and stored in cloud without privacy leakage . Specifically, we first design privacy-preserving computation protocols over rational numbers to guarantee computation accuracy and handle outsourced operations on-the-fly. Then, we demonstrate that PHPR system achieves secure disease predictor. Finally, the experimental results using real-world datasets demonstrate that PHPR system not only provides secure disease predictor over ciphertexts, but also maintains the prediction accuracy as the original classifier.

Cloud-assisted Internet of Things (IoT) is increasingly prevalent in our society, for example in home and office environment; hence, it is also known as cloud-assisted Internet of Everything (IoE). While in such a setup, data can be easily shared and disseminated (e.g., between a device, such as Amazon Echo and the cloud, such as Amazon AWS), there are potential security considerations that need to be addressed. Thus, a number of security solutions have been proposed. For example, searchable encryption (SE) has been extensively studied due to its capability to facilitate searching of encrypted data. However, threat models in most existing SE solutions rarely consider the malicious data owner and semi-trusted cloud server at the same time, particularly in dynamic applications. In a real-world deployment, disputes between above two parties may arise as either party will accuse the other of some misbehavior. Furthermore, efficient full-update operations (e.g., data modification, data insertion, and data deletion) are not typically supported in the cloud-assisted IoE deployment. Therefore, in this paper, we present a fair and dynamic data sharing framework (FairDynDSF) in the multiowner setting. Using FairDynDSF, one can check the correctness of search results, achieve fair arbitration, multikeyword search, and dynamic update. We also prove that FairDynDSF is secure against inside keyword guessing attack and demonstrate its efficiency by evaluating its performance using various datasets.