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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 $\mathcal {O}(n)$ O ( n ) and $\mathcal {O}(n\log n)$ O ( n log n ) for a single and multiple task participants selection problems, respectively.

With the outbreak of e-mail message leakage events, such as the Hillary Clinton's Email Controversy, privacy and security of sensitive e-mail information have become users' primary concern. Encrypted email seems to be a viable solution for providing security, but it will greatly limit their operations. Public encryption with keyword search (PEKS) scheme is a popular technology to incorporate security protection and favorable operability functions together, which can play an important role in searching over encrypted email in a cloud server. In this paper, we propose a practical PEKS scheme named as public-key multi-keyword searchable encryption with hidden structures (PMSEHS). It could enable e-mail receivers to do the multi-keyword and boolean search in the large encrypted email database as fast as possible, without revealing more information to the cloud server. We also give comparative experiments, which demonstrate that our scheme has a higher efficiency in multi-keyword search for encrypted emails.

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.

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.

Recently, more and more data have been stored in the cloud with keyword indices so that the data users can make search over the databases. In some of these database applications, the query frequency analysis of keywords is quite important to the optimization of the databases and it is easy to be implemented when the data are not encrypted. However, with the growing demands of data privacy, it is desired that the data should be encrypted before uploaded to the cloud. Searchable encryption has been proposed which enables users to make keyword search over the encrypted data with keyword privacy. In a secure searchable encryption scheme, it is required that the keyword in each query should not be revealed. So it becomes challenging to analyze the query frequency of keywords in an encrypted database which has the pressing need of optimization. In this paper, we first consider this problem and present an efficient solution to it which enables the query frequency analysis of keywords without destroying the privacy of the encrypted data and the identity privacy of data users. We also simulate our solution and show that it is practical to the real applications.

Reversible data hiding (RDH) can be used to imperceptibly embed data into images in a reversible manner. Many RDH schemes have been developed for uncompressed images. However, JPEG compressed images are more widely used in our daily lives. The existing RDH techniques for JPEG images may cause significant distortion or a large increase in the file size of marked images. In this paper, a novel RDH scheme for JPEG images is proposed. First, the negative influence models of data embedding, including image visual distortion model and file size change model, are mathematically established. Then, a negative index for each frequency is defined as the weighted sum of the normalized average image visual distortion and the normalized average file size change per 1-bit hidden data, and the frequencies with small negative indices will be used for data embedding with a high priority. The weighting factor can be adjusted according to the user’s preference for less image distortion or smaller file size. Lastly, secret data is embedded into non-zero quantized AC coefficients of the selected frequencies in ascending order of zero-run length. Extensive experiments conducted on typical images and a well-known image database show that the presented negative influence models are effective, and the proposed RDH scheme based on the models can achieve low image distortion and small increase in the file size of marked images.

As cloud storage becomes more common, concerns about the invasion of privacy are increasing. When images are stored in an encrypted form in the public cloud, reversible data hiding in the encrypted domain can be applied to embed additional data within the encrypted images for ease of management. Most existing works focus on uncompressed images and are not applicable to JPEG images, which are widely used throughout the Internet. Therefore, in this paper, a novel reversible data hiding scheme for encrypted JPEG bitstreams is proposed. First, an effective method of bitstream-based JPEG image encryption is employed to encrypt plaintext JPEG images. Then, we present a reversible data hiding technique for encrypted JPEG images based on invariant zero-run length in the zero-run value pairs. In the cloud, additional data, such as labels, timestamps, origins, and authentication messages, are directly embedded into the encrypted JPEG images with our proposed data hiding technique. From the marked encrypted JPEG images, the extraction of the hidden data and the recovery of the original images can be done independently. Extensive experiments performed on several typical images and three well-known image databases show that the proposed scheme can achieve much higher embedding capacity than that of most recent schemes, and the file sizes of the marked encrypted JPEG images are well preserved compared to those of the related methods. In addition, we provide further analysis to show that the proposed scheme has good format compatibility and low computational complexity.

The large quantity of mobile devices equipped with various built-in sensors and the easy access to the high-speed wireless networks have made spatial crowdsourcing receive much attention in the research community recently. Generally, the objective of spatial crowdsourcing is to outsource location-based sensing tasks (e.g., traffic monitoring and pollution monitoring) to ordinary mobile workers (e.g., users carrying smartphones) efficiently. In this paper, we study a reliable task assignment problem for spatial crowdsourcing in a large worker market. Specifically, we use worker confidence to represent the reliability of successfully completing the assigned sensing tasks, and we formulate two optimization problems, maximum reliability assignment (MRA) under a recruitment budget and minimum cost assignment (MCA) under a task reliability requirement. We reveal the special structure properties of these problems, based on which we design effective approaches to assign tasks to the most suitable workers. The performances of the proposed algorithms are verified by theoretic analysis and experimental results on both real and synthetic datasets.

Task matching in crowdsourcing is designed to provide convenient task information retrieval and has been extensively explored. In general, the task matching process is required to be reliable and to meet privacy requirements. However, most existing privacy-preserving task matching solutions for crowdsourcing focus on privacy issues but ignore the reliability of the process. In this paper, we propose a blockchain-based task matching scheme for crowdsourcing with a secure and reliable matching. Instead of utilizing a centralized cloud server, we employ smart contracts, an emerging blockchain technology, to provide reliable and transparent matching. In this way, data confidentiality and identity anonymity are achieved effectively and efficiently. The extensive privacy analysis and performance evaluation show that our solution is secure and feasible.

Mobile crowdsensing (MCS) is an emerging data collection paradigm that exploits the potential of individual mobile devices to acquire mass data in a cost-effective manner. One of the important challenges in MCS application is to resist malicious users who provide false data to disturb the system. In the existing work, the reputation management scheme is an effective way to overcome the challenge. However, most reputation management schemes rely on a semi-honest server and process data in the plaintext domain without considering server security and user privacy. In this paper, we integrate the blockchain and edge computing in the MCS scenario to construct a credible and efficient blockchain-based MCS system, called BC-MCS. To resist malicious users, we present a privacy-preserving reputation management scheme based on the proposed system. Furthermore, we design a delegation protocol to solve the inherent problem of user dynamics in the MCS. The prototype system implemented on the Hyperledger Sawtooth and Android client demonstrates that our scheme can achieve higher utility and security levels in handling malicious users compared with the previous centralized reputation management schemes.

Image encryption techniques can be used to ensure the security and privacy of valuable images. The related works in this field have focused more on raster images than on compressed images. Many existing JPEG image encryption schemes are not quite well compatible with the JPEG standard, or the file size of an encrypted JPEG image is apparently increased. In this paper, a novel bitstream-based JPEG image encryption method is presented. First, the groups of successive DC codes that encode the quantized DC coefficient differences with the same sign are permuted within each group. Second, the left half and the right half of a group, whose size will increase with the number of iterations, of consecutive DC codes may be swapped with each other, depending on whether an overflow of quantized DC coefficients occurs during decoding. Third, all AC codes are classified into 63 categories according to their zero-run lengths, then the AC codes within each category are, respectively, scrambled. Finally, all MCUs, except for DC codes, are randomly shuffled as a whole. Moreover, an image-content-related encryption key is employed to provide further security. The experimental results show that the file size of an encrypted JPEG image is almost the same as that of the corresponding plaintext image except for slight variations because of byte alignment. In addition, the quantized DC coefficients decoded from an encrypted JPEG image will not fall outside the valid range. Improved format compatibility is provided compared with other related methods. Moreover, it is unnecessary to perform entropy encoding again because all of the encryption operations are performed directly on the JPEG bitstream. The proposed method proves to be secure against brute-force attacks, differential cryptanalysis, known plaintext attacks, and outline attacks. Our proposed method can also be applied to color JPEG images.

Wireless sensor networks (WSNs) are increasingly gaining impact on our daily lives. They are finding a wide range of applications in various domains, such as health-care, environmental monitoring, and so on. In future, WSNs are expected to be integrated into the Internet of Things (IoT). The integrity of the sensed data is of primary importance in WSN and IoT applications. However, WSN platforms always have very limited resources in terms of battery power, computing, and memory. Therefore, it is a key challenge to design an energy-friendly, lightweight digital signature algorithm for WSN platforms. In this paper, we present precomputation methods for unbalanced oil-vinegar (UOV) signature scheme by exploiting the energy-harvesting capabilities of WSN to enhance the performance of UOV signature. In addition, we combine a circulant method with precomputation in the UOV signature to further reduce the energy cost. Meanwhile, the circulant method reduces the size and the memory overhead of the precomputation tuple. This increases the availability of precomputation and greatly enhances the availability of the overflow energy. By integrating the above-mentioned optimization methods, the cost of UOV signature in a WSN node can be reduced by 93%. This greatly enhances the performance of UOV signature, making it feasible for the practical deployment on resource-constrained wireless sensor platforms.

A publicly verifiable secret sharing (PVSS) scheme enjoys the public verification and the lower cost of communication than VSS scheme. However, the existing PVSS schemes cannot be applied in the scenarios of the devices with low computation ability and do not share the multiple secrets among all participants efficiently. In this paper, an efficient publicly verifiable multi-secret sharing scheme with outsourcing secret reconstruction is proposed. Each participant only spends a small amount of computational cost to recover multiple secrets because of the expensive burden of computation and verifiability is outsourced to the cloud service provider (CSP). Moreover, the CSP knows no information of the secrets, and the participants have the abilities to verify the returned result. We also prove that our scheme is secure under the hardness assumption of the discrete logarithm problem and the modified generalized bilinear inversion problem.

Spatial crowdsourcing and crowdsensing are two emerging crowdsourcing paradigms, which enable a variety of location-based query and sensing tasks. In spatial crowdsourcing, mobile workers are required to travel physically to target locations in order to complete query tasks. Most existing work, hence, has focused on designing efficient query task assignment schemes to maximize the task completion rate under traveling constraints of workers for spatial crowdsourcing systems. In crowdsensing, on the other hand, sensor recordings of workers’ smartphones are of interest and have been collected to build various applications. Therefore, work concerning crowdsensing has strived to maximize the coverage area of sensor trajectories by selecting a set of workers. In this paper, we investigate the integration of these two paradigms. We notice a key link between these paradigms: While a worker is traveling to the target location of a query task, his trajectory may provide valuable coverage for a sensing task. Therefore, we propose a task management framework, named SpatialRecruiter, to efficiently match workers to the merged query and sensing tasks. We propose two coverage estimation functions to compute the coverage potential of a worker. Then, we design a greedy heuristic to select and assign workers. The experimental results on a real-world dataset demonstrate that the proposed strategies are efficient and effective in meeting the requirements of both paradigms.

Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires only lightweight computations like modular multiplications. We present a general OOPDP transformation framework which is applicable to PDP-related schemes with metadata aggregatability and public metadata expansibility . Following the framework, we present two efficient OOPDP instantiations. Technically, we present aggregatable vector Chemeleon hash functions which map a vector of values to a group element and play a central role in the OOPDP transformation. Theoretical and experimental analyses confirm that our technique is practical to speed-up PDP schemes.

Rainbow is one of the most important signature schemes in multivariate public key cryptography. It enjoys a strong security guarantee and is a promising signature scheme in Post-Quantum Cryptography. However, it suffers from large key size. In this paper, we propose Circulant Rainbow with shorter private key and higher signing efficiency. In Circulant Rainbow, we introduce rotating relations into parts of Rainbow private key to speed up the signing procedure and reduce the private key size. We carefully choose security parameters so that our Circulant Rainbow is secure against all known attacks. In our experiment, Circulant Rainbow is about three times faster than original Rainbow and it can reduce the private key size by about 45%. We also make a comparison of Circulant Rainbow with some traditional signature schemes, the results show that Circulant Rainbow is a promising candidate in Post-Quantum Cryptography.

Access control is an indispensable security component of cloud computing, and hierarchical access control is of particular interest since in practice one is entitled to different access privileges. This paper presents a hierarchical key assignment scheme based on linear-geometry as the solution of flexible and fine-grained hierarchical access control in cloud computing. In our scheme, the encryption key of each class in the hierarchy is associated with a private vector and a public vector, and the inner product of the private vector of an ancestor class and the public vector of its descendant class can be used to derive the encryption key of that descendant class. The proposed scheme belongs to direct access schemes on hierarchical access control, namely each class at a higher level in the hierarchy can directly derive the encryption key of its descendant class without the need of iterative computation. In addition to this basic hierarchical key derivation, we also give a dynamic key management mechanism to efficiently address potential changes in the hierarchy. Our scheme only needs light computations over finite field and provides strong key indistinguishability under the assumption of pseudorandom functions. Furthermore, the simulation shows that our scheme has an optimized trade-off between computation consumption and storage space.

More and more clients would like to store their data to public cloud servers (PCSs) along with the rapid development of cloud computing. New security problems have to be solved in order to help more clients process their data in public cloud. When the client is restricted to access PCS, he will delegate its proxy to process his data and upload them. On the other hand, remote data integrity checking is also an important security problem in public cloud storage. It makes the clients check whether their outsourced data are kept intact without downloading the whole data. From the security problems, we propose a novel proxy-oriented data uploading and remote data integrity checking model in identity-based public key cryptography: identity-based proxy-oriented data uploading and remote data integrity checking in public cloud (ID-PUIC). We give the formal definition, system model, and security model. Then, a concrete ID-PUIC protocol is designed using the bilinear pairings. The proposed ID-PUIC protocol is provably secure based on the hardness of computational Diffie–Hellman problem. Our ID-PUIC protocol is also efficient and flexible. Based on the original client’s authorization, the proposed ID-PUIC protocol can realize private remote data integrity checking, delegated remote data integrity checking, and public remote data integrity checking.

Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

With the growth of public demand for online access to health services, many efforts have been devoted to personal health records (PHR) in cloud computing. It enables patients to manage their personal health information (PHI) in cloud servers, which greatly facilitates the collection, access and sharing of PHI. Since cloud servers are not fully trusted, it is desirable that the PHI can be encrypted for privacy protection before uploaded to the cloud. Besides the privacy of PHI, fine-grained and flexible search control is also strongly desired for a secure PHR system. In this article, we first present attribute set based keyword search (ASBKS) which can realize fine-grained keyword search of encrypted PHR. Compared with the existing searchable encryption with access control, the proposed ASBKS can achieve more flexibility in user attributes organization and more efficiency in specifying policies. Furthermore, we present a hierarchical ASBKS scheme to improve scalability by extending ASBKS with a hierarchical structure of users. We implement our ASBKS scheme and the experimental results demonstrate that it is both efficient and flexible for encrypted PHR in cloud computing.

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Secure group communication systems have become increasingly important for many emerging network applications. An efficient and robust group key management approach is indispensable to a secure group communication system. Motivated by the theory of hyper-sphere, this paper presents a new group key management approach with a group controller (GC). In our new design, a hyper-sphere is constructed for a group and each member in the group corresponds to a point on the hyper-sphere, which is called the member’s private point. The GC computes the central point of the hyper-sphere, intuitively, whose “distance” from each member’s private point is identical. The central point is published such that each member can compute a common group key, using a function by taking each member’s private point and the central point of the hyper-sphere as the input. This approach is provably secure under the pseudo-random function (PRF) assumption. Compared with other similar schemes, by both theoretical analysis and experiments, our scheme (1) has significantly reduced memory and computation load for each group member; (2) can efficiently deal with massive membership change with only two re-keying messages, i.e., the central point of the hyper-sphere and a random number; and (3) is efficient and very scalable for large-size groups.

Wireless sensor networks (WSNs) are widely applicable in monitoring and control of environment parameters. It is sometimes necessary to disseminate data through wireless links after they are deployed in order to adjust configuration parameters of sensors or distribute management commands and queries to sensors. Several approaches have been proposed recently for data discovery and dissemination in WSNs. However, they all focus on how to ensure reliability and usually overlook security vulnerabilities. This paper identifies the security vulnerabilities in data discovery and dissemination when used in WSNs. Such vulnerabilities allow an adversary to update a network with undesirable values, erase critical variables, or launch denial-of-service (DoS) attacks. To address these vulnerabilities, this paper presents the design, implementation, and evaluation of a secure, lightweight, and DoS-resistant data discovery and dissemination protocol named SeDrip for WSNs. Our protocol takes into consideration the limited resources of sensor nodes, packet loss and out-of-sequence packet delivery. Also, it can provide instantaneous authentication without packet buffering delay, and tolerate node compromise. Besides the theoretical analysis that demonstrates the security and performance of SeDrip, this paper also reports the experimental evaluation of SeDrip in a network of resource-limited sensor nodes, which shows its efficiency in practice. 2013 IEEE. Scopus 2-s2.0-84884905327