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Self-organizing vehicular social networks underpin many location-based services (LBS) such as those that collect and share environmental information (e.g., traffic and weather conditions) among vehicular users and the infrastructure. There are, however, security and privacy considerations in the sharing of such information, and one popular approach is to design lightweight authentication solutions for LBS. Existing approaches may suffer from limitations such as significant computational and/or storage overheads, latency and time delays, and consequently impractical for resource-constrained on-board units. In this paper, we propose an efficient privacy-preserving LBS bundle authentication scheme (hereafter referred to as SAVE) through secure redundancy filtering in self-organizing vehicular social networks. Firstly, an enhanced self-healing key distribution protocol with distributed revocation is proposed to reduce communication cost for retransmitting lost key material and resist free-riding attacks to enhance the authentication efficiency. Then, based on it, a generalized version of online/offline aggregate signature is proposed to achieve batch LBS bundle verification based on arbitrary one-way function holding the property of multiplicative homomorphism. Finally, an efficient zero-knowledge range proof based on lightweight one-way hash chain is designed to decide the redundancy of LBS bundles without disclosing vehicular users' location privacy. Formal security proof and extensive simulation results demonstrate that our proposed SAVE achieves identity privacy, two levels of location privacy and the practicability in reality.

Smart grids bring advantages of reliabilty and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and securtiy in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the paper. Firstly, we adopt Chinese Remainder Theorem to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center. Secondly, our scheme is efficient for encryption without high-cost additive homomorphic public key encryption scheme, such as Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computation complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity and data integrity of the users’ reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than existing competing approaches in terms of computation and communication overhead.

With the rapid development of computer networks, the network architecture has been more and more complex and diverse. Smart grid is a kind of wireless sensor network consuming energy in smart city. Smart grid communications have developed rapidly in recent years, while the privacy, integrity and confidentiality of user data have been the main security issues. However, lattice-based homomorphic cryptography can resist quantum attacks and effectively provide the security of the scheme. In this paper, we proposed an aggregate signature scheme based on new Batch RSA. After that, a data aggregation scheme is proposed based on this aggregate signature scheme and the qualified homomorphic cryptosystem. Compared with the previous schemes, our scheme is more secure because we implement seven properties, such as post quantum, privacy and so on. Compared with the representative scheme proposed by Abdallah et al., our scheme has lower computation and communication overhead. In addition, the cryptographic algorithms involved in our scheme are lightweight. The network model in our scheme is suitable for large intelligent communities. So our scheme is very suitable for residential area network.

Signal processing in the encrypted domain is a potential tool to protect sensitive signals against untrusted cloud servers and malicious users in the delegated computing setting, without affecting the accuracy of signal analyzing and processing. Most existing approaches use Paillier's public key additively homomorphic encryption to encrypt each signal in a large bundle; thus, incurring significant computational costs at local, often resource-constrained, devices while guaranteeing only signal input privacy. To address these limitations, in this paper, an efficient privacy-preserving outsourced discrete wavelet transform scheme (PPDWT), comprising PPDWT-1 and PPDWT-2, without leveraging public key (fully) homomorphic encryption is proposed. Specifically, they are respectively proposed to achieve signal input privacy, coefficient privacy and discrete wavelet transform result privacy against the collusion between honest-but-curious cloud and malicious users. Both constructions leverage the offline execution of any one-way trapdoor permutation only once to encrypt batch signals, and permit signal processing in the encrypted domain. We also discuss the expanding factor, the upper bound and various extensions to privacy-preserving discrete cosine/fourier transform in the encrypted domain. Finally, our proposed PPDWT is formally proved secure under the universal composability (UC) model. We then evaluate the proposed approach using case studies to demonstrate its effectiveness and practicability.

Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary pre-steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under various encryption mechanisms when data owners try to secure the confidentiality of the data. This makes data aggregation extremely challenging, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching service that securely bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data, i.e., after encryption switching one may search over the (outsourced) data without loss of data and query secrecy. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme may be applicable in multi-domain cloud storage system.

With the fast development of Internet of Things (IoT) especially for smart grid and electric vehicle (EV) networking, vehicle-to-grid (V2G) communications have been increasingly studied and recognized as one of the most convincing tools for general road transportation, to effectively reduce the oil demands and gas emissions. Unfortunately, a series of security and privacy issues have significantly impeded its wide adoption. The existing work mainly focused on the static environment, which cannot be directly applied to the mobile setting where EVs travel across regions. The dynamic pricing metric in V2G networks depends on the real-time electricity usage aggregation in one region. To address this issue, in this article, an efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption. In the proposed threshold homomorphic encryption, a legal ciphertext can be generated if and only if no less than threshold $k$ individual illegal ciphertexts are aggregated. Therefore, the aggregated power consumption data can be successfully decrypted while the individual power consumption privacy of honest EV users can be well protected against even the collusion between a malicious power charging station and compromised EVs. Furthermore, the technique of SASD guarantees entity authentication with a minimized amount of transmitted data. Finally, formal security proof and extensive performance evaluation demonstrate the effectiveness and practicability of our proposed PADP.

It is a challenging issue in smart grid to provide flexible and efficient data aggregation and at the same time maintain data integrity and data privacy. To address this issue, we firstly propose a lightweight $t$ -times homomorphic encryption scheme, which can reduce the computational cost of smart devices further and resist quantum attacks. Based on it, we propose two efficient data aggregation schemes for two application scenarios. The first scheme is suitable for small network, such as home network and small-office network. The second scheme is appropriate for medium-size network, such as a part of smart city. More specifically, we explore how the number of smart devices and the number of home area networks affect the overhead of communication and computation. The experiment results show that the proposed schemes are efficient. Moreover, the proposed schemes not only meet security requirements of integrity, confidentiality, authenticity, resistance to intermediate attack, resistance to replay attack, forward and backward security and resistance to quantum attack, but also reduce the overhead of communication and computation.

The demands for the fairness, security, and efficiency of the supply chain have grown significantly due to the rise of globalization. However, some problems of the information flow, logistics, and capital flow in the supply chain remain a challenge, such as the information asymmetry between upstream and downstream, substandard quality of goods, difficulty in traceability, and default of payment. Therefore, this paper proposes a blockchain-based supply chain framework (SESCF), which solves the supply chain problems securely and efficiently. First, the use of blockchain and smart contracts ensures the information symmetry in the supply chain system. Second, the radio frequency identification (RFID) provides a unique identity of goods, which helps in real-time quality monitoring. Additionally, the immutability and distributed storage of the blockchain play an important role in tracking the origin of goods. Third, the efficient payment channel is used to solve the problem of payment defaults. Furthermore, simulations of smart contracts along with the security analyses are presented in this paper. We also implement a blockchain-based supply chain system (SescfDapp), which is built upon a Consortium blockchain. Large-scale experiments and detailed analysis prove the feasibility and efficiency of our proposed system.

Searchable encryption (SE) allows the cloud server to search over the encrypted data and leak information as little as possible. Most existing efficient SE schemes assume that the leakage of search pattern and access pattern is acceptable. A series of work was proposed, instructing malicious users to use this leakage to come up with attacks. Especially, with a devastating attack proposed by Zhang et al., the cloud server can reveal the keywords queried by normal users by using some injected files. From the method of constructing uniform $(k,n)$-set of a finite set $A$ proposed by Cao, we put forward a new file-injection attack. In our attack, the server needs fewer injected files than the previous attack when the size of $T$ is larger than 9 and the size of keyword set is larger than $2T$, where $T$ is the threshold of the number of keywords in each injected file. Our attack is more practical and easier to implement in the real scenario.

ABKS has drawn much attention from research and industry in recent years, an ABKS scheme is an encryption scheme that supports keyword search and access control. Attribute-Based Encryption is a public key encryption that enables users to encrypt and decrypt message based on attributes. In a typical implementation, the size of the ciphertext is proportional to the number of attributes associated with it and the decryption time is proportional to the number of attributes used during decryption. Inherit from ABE technology, the computation cost and ciphertext size in most ABKS schemes grow with the complexity of the access policy. On the other hand, we found that the traditional ABKS schemes cannot resist our secret-key-recovery attack. To deal with the above problems, we present new ciphertext policy attribute based encryption with fast keyword search constructions. Our constructions preserve the fine-grained access control inherited from the ABE system while supporting hidden policy and fast keyword search. Our constructions feature multi-value-independent compared with the existing attribute based searchable encryption schemes. The performance analysis demonstrates the efficiency of our constructions. We offer rigorous security proof of our second scheme, which is IND-CKA and IND-CPA secure.

Location-based service (LBS) in vehicular ad hoc networks (VANETs) has significantly benefited information acquisition from geographically-based social networking. Authentication guarantees the unforgeability and the effectiveness of the LBS information. Unfortunately, owing to a large quantity of redundant or useless LBS messages disseminated in VANETs, the heavy authentication overhead of the existing work adopting a periodically released authentication key, filtering with message identifiers or exploiting public key (fully) homomorphic encryption (FHE), is either intolerable by resource-constrained on-board units (OBUs) or inappropriate to the realtime controlling requirement for VANETs. In this paper, an efficient multi-key secure outsourced computation scheme MSOC without exploiting public key FHE is first proposed, in the setting of two non-colluding servers, namely the cloud and the cryptographic service provider (CSP). Then, based on MSOC, an efficient and secure comparison protocol LSCP is devised, without the interaction between the server and the users. Furthermore, a lightweight privacy-preserving authentication protocol LPPA for LBS in VANETs is proposed, by eliminating duplicate and useless encrypted LBS messages before authentication is executed, through a newly devised efficient privacy-preserving information filtering system. Both user’s location privacy and interest privacy are well protected against even the collusion between the roadside units (RSUs) serving as the cloud (or CSP) and malicious users. Especially, the property of ciphertext re-encryption of our proposed MSOC also guarantees the interest pattern privacy whether two users accept the same LBS information. Finally, formal security proof and extensive simulation results verify the effectiveness and practicability of our proposed LPPA.

M-healthcare social networks are becoming increasingly important in supporting an efficient and promising e-healthcare platform. The moving patients suffering from the same disease or living in the neighborhood of the same healthcare provider constitute a delay tolerant network (DTN) to help each other forwarding the patient health information (PHI). Unfortunately, the mobile device carried on each patient collecting PHI from body sensors is subjected to sophisticated attacks. To reduce the probability of patients being threatened by a newly identified target node compromise attack, maximize the credits earned from transmitting packets and achieve the fairness among patients, a game theory based secure incentive mechanism GTSIM is proposed. Based on it, by exploiting the complete marriage theory in combinatorics, a patient-optimized privacy-preserving packet forwarding scheme POP is devised to protect the PHI confidentiality and the identity/location privacy of the patients, decrease the privacy exposure when intermediate patients are compromised and realize the property of patient-optimization required in m-healthcare systems. Last but not least, the security analysis shows both our proposed GTSIM and POP can resist various sophisticated attacks and the extensive simulation demonstrates the practicability and efficiency with high message delivery ratio, obtainable utility fraction and low average latency.

Real-time bidding (RTB), one of the major trading mechanisms used for online advertising, allows the advertiser to make an impression-level bid decision. However, the security and privacy concerns of RTB are gaining increasing attention with the recent enforcement of the European Union General Data Protection Regulation (GDPR). In this study, we present a novel privacy-preserving RTB (Pri-RTB) protocol, which aims at preserving both data privacy and utility by performing additively homomorphic encryption on the user profile. To our knowledge, this is the first work to address the privacy issue of the RTB paradigm. We present a formal proof for the security of Pri-RTB under the assumption of the decisional Diffie–Hellman (DDH) problem. We analyze and elaborate the superiority of Pri-RTB over other related works. We also developed a prototype for Pri-RTB and conducted several experiments to evaluate its feasibility and efficiency under different parameters. Our experiments demonstrate that Pri-RTB can work highly efficiently in a practical setting (for example, a scenario with approximately 200 advertisers per auction).

In this paper, we introduce a new cryptographic primitive, called autonomous path proxy re-encryption (AP-PRE), which is motivated by several application scenarios where the delegator would like to control the whole delegation path in a multi-hop delegation process. Compared with the traditional proxy re-encryption, AP-PRE provides much better fine-grained access control to delegation path. Briefly speaking, in an AP-PRE scheme, the delegator designates a path of his preferred delegatees. The path consists of several delegatees with the privilege from high to low. If the delegatee in the path cannot complete the decryption, the decryption right is automatically delegated to the next one in the path. In this way, the delegator can ensure that the delegation has always been done among those delegatees the delegator trusts. Moreover, an AP-PRE scheme has to obey the following path rules. The delegation, for ciphertexts of a delegator $i$i, can only be carried out on the autonomous path $\mathsf {Pa}_i$Pai designated by the delegator $i$i, in the sense that (1) re-encrypted ciphertexts along the autonomous path $\mathsf {Pa}_i$Pai cannot branch off $\mathsf {Pa}_i$Pai with meaningful decryption, and (2) original ciphertexts generated under $pk_j$pkj for $j \ne i$j≠i (i.e., for a path $\mathsf {Pa}_j$Paj different from $\mathsf {Pa}_i$Pai) cannot be inserted into (i.e., cannot be transformed along) the autonomous path $\mathsf {Pa}_i$Pai with meaningful decryption. We give the formal definition, as well as the formal security model, for this cryptographic primitive. Under this concept, we construct an IND-CPA secure AP-PRE scheme under the decisional bilinear Diffie-Hellman (DBDH) assumption in the random oracle model. Our scheme is with the useful properties of proxy re-encryption, i.e., unidirectionality and multi-hop.

Privacy-protected signal and image processing for outsourced computing has hence recently been popular. We present a framework for privacy-preserving outsourced image feature Extraction(POIFE) in the semi-honest setting. Also we prove that the construction is simulation-based secure under the semi-honest adversaries. In our scheme, the user can be flexible to outsource his computation to the cloud without real-time online. Compared with the previous works, our solution improves privacy.

Autonomous vehicles are expected to significantly enhance the human mobility. However, recently researchers have discovered and demonstrated some attacks on vehicles, which have caused a panic among the public. Furthermore, these attacks have demonstrated that the security issue is still one of the major challenges of vehicles. In this paper, we propose a novel edge computing based anomaly detection, coined edge computing based vehicle anomaly detection (EVAD), which exploits edge based sensor data fusion to identify the anomaly events. The time domain property, i.e., the correlation between different intra-vehicle sensors, and the frequency domain property of sensor data are utilized to judge whether an anomaly has occurred within the vehicle. Especially, to reduce the computation overhead and improve the performance, multiple sensors will be organized as ring architecture, which is a tradeoff of detection accuracy and complexity. In addition, the major components (e.g., anomaly detection module) of EVAD are embedded in edge computing devices, which make the anomaly detection be more efficient and privacy preserving. Meanwhile, a more appropriate model is generated on the cloud server, of which computation overhead maybe heavy for edge computing devices. This paper evaluates the performance of EVAD under different scenarios, and the experimental results demonstrate its feasibility and efficiency. The average true positive rate achieves 99.5% with 1% false positive rate.

As one of the promising information-centric networking (ICN) architectures, NDN has attracted tremendous research attention and effort in the past decade. In particular, security and privacy remain significant concerns and challenges due to the fact that most of the traditional cryptographic primitives are no longer suitable for NDN architecture. For example, the traditional cryptographic primitives aim to secure point-to-point communications, always requiring explicit descriptions of to where or whom the data packets are intended, while network addressing or locating in NDN becomes implicit. To deal with such issues, the recently developed cryptographic primitives such as ABE have been applied to NDN. Also, to efficiently solve the trust-roots problem and seamlessly deploy cryptographic infrastructures, the concept of SDN has been introduced to NDN as well. This tutorial is devoted to exploring the interesting integration between NDN, ABE, and SDN.

Public key encryption scheme with keyword search (PEKS) is a promising technique supporting search on encrypted data without leaking any information about the keyword. In real applications, it's critical to find an effective revocation method to revoke users in multi-user cryptosystems, when user's secret keys are exposed.In this paper, we propose the first designated server-aided revocable identity-based encryption scheme with keyword search (dSR-IBKS) from lattice. The dSR-IBKS model requires each user to keep just one private key corresponding with his identity and does not need to communicate with the key generation center or the server during key updating. We have proved that our scheme can achieve chosen keyword indistinguishability in the standard model. In particular, our scheme can designate a unique tester to test and return the search results, therefore no other entity can guess the keyword embedded in the ciphertext by generating search queries and doing the test by itself. We provide a formal security proof of our scheme assuming the hardness of the learning with errors (LWE) problem on the standard model.

With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search. In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes.

Searchable Symmetric Encryption(SSE) remains to be one of the hot topics in the field of cloud storage technology. However, malicious servers may return incorrect search results intentionally, which will bring significant security risks to users. Therefore, verifiable searchable encryption emerged. In the meantime, single-keyword query limits the applications of searchable encryption. Accordingly, more expressive searchable encryption schemes are desirable. In this paper, we propose a verifiable conjunctive keyword search scheme based on Cuckoo filter (VCKSCF), which significantly reduces verification and storage overhead. Security analysis indicates that the proposed scheme achieves security in the face of indistinguishability under chosen keyword attack and the unforgeability of proofs and search tokens. Meanwhile, the experimental evaluation demonstrates that it achieves preferable performance in real-world settings.

Attribute-based encryption received widespread attention as soon as it proposes. However, due to its specific characteristics, the attribute-based access control method is not flexible enough in actual operation. In addition, since access authorities are determined according to users' attributes, users sharing the same attributes are difficult to distinguish. Once a malicious user makes illicit gains by their decryption authorities, it is difficult to trace specific users. This paper follows the practical demand to propose a more flexible key-policy attribute-based encryption scheme with black-box traceability. The scheme has a constant number of constant parameters which can be utilized to construct attribute-related parameters flexibly, and the method of traitor tracing in broadcast encryption is introduced to achieve effective malicious user tracing. In addition, the security and feasibility can be proved by the security proofs and performance evaluation in this paper.

Images play an increasingly important role in our lives as carriers of Information. At the same time, personal devices are increasingly unable to store and compute large amounts of images, so it is necessary to outsource to cloud servers. However, this also brings privacy issues, such as personal photos, medical photos, map mapping, etc. In this paper, we present a content-based image retrieval (CBIR) scheme that can be performed on ciphertext images. In order to better represent the Lage, instead of the traditional algorithms such as SIFT and HOG, image features are extracted using fine-tuned convolutional neural networks, and then outsources the encrypted feature vector and ciphertext image to cloud server. Considering the efficiency of the search, this paper uses the k-means algorithm to and local sensitive hash function to build a secure tree index. This paper proposes a new functional encryption of the inner product to calculate the Euclidean distance between the feature vectors to obtain the similarity between the images, and finally return the ciphertext image satisfying the condition to the user. The experimental results show the efficiency of our program, in addition, the paper gives security analysis to prove that our solution can against Chosen-Plaintext Attack (CPA).

© 2014 IEEE. Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this article, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized NNs (DiNNs) is proposed. First, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain, including multivariate polynomial, nonlinear activation function, gradient function, and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for DiNNs is proposed, which can also be extended to convolutional NN. Finally, we give the formal security proofs for dataset privacy, model training privacy, and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in DiNN to demonstrate the high efficiency and accuracy of our proposed LPTE.

With the development of technology, the preservation of digital evidence becomes increasingly important in case investigations. To maintain the authenticity of an evidence, its entire lifecycle has to be recorded. In addition, traditional database technologies are not able to maintain the integrity and authenticity of digital evidence. In order to achieve authentication and integrity, as well as confidentiality, of digital evidence, we propose a protocol for digital evidence chain of custody based on revocable ciphertextpolicy attribute-based encryption, BLS signature, and blockchain technology. In our protocol, attribute-based encryption is used to achieve fine-grained access control and BLS signature is used to verify digital evidence. Besides, we use blockchain technology to ensure the integrity and traceability of digital evidence. Analysis and experimental results show that the proposed protocol, which well balances the privacy and the traceability, guarantees the integrity and validity of evidence.

The tight security bound of the KAC (Key-Alternating Cipher) construction whose round permutations are independent from each other has been well studied. Then a natural question is how the security bound will change when we use fewer permutations in a KAC construction. In CRYPTO 2014, Chen et al. proved that 2-round KAC with a single permutation (2KACSP) has the same security level as the classic one (i.e., 2-round KAC). But we still know little about the security bound of incompletely-independent KAC constructions with more than 2 rounds. In this paper, we will show that a similar result also holds for 3-round case. More concretely, we prove that 3-round KAC with a single permutation (3KACSP) is secure up to \(\varTheta (2^{\frac{3n}{4}})\) queries, which also caps the security of 3-round KAC. To avoid the cumbersome graphical illustration used in Chen et al.’s work, a new representation is introduced to characterize the underlying combinatorial problem. Benefited from it, we can handle the knotty dependence in a modular way, and also show a plausible way to study the security of rKACSP. Technically, we abstract a type of problems capturing the intrinsic randomness of rKACSP construction, and then propose a high-level framework to handle such problems. Furthermore, our proof techniques show some evidence that for any r, rKACSP has the same security level as the classic r-round KAC in random permutation model.

We develop two variants of Cocks’ identity-based encryption. One variant has faster encryption, where the most time-consuming part only requires several modular multiplications. The other variant makes the first variant anonymous under suitable complexity assumptions, while its decryption efficiency is about twice lower than the first one. Both the variants have ciphertext expansion twice more extensive than the original Cocks’ identity-based encryption. To alleviate the issue of the second variant’s large ciphertext expansion, we consider using it to construct a public-key encryption with keyword search scheme with a fast encryption algorithm by means of the transform in [1].

Ciphertext-policy attribute-based encryption (CP-ABE) has been proposed to enable fine-grained access control on encrypted data for cloud storage service. In the context of CP-ABE, since the decryption privilege is shared by multiple users who have the same attributes, it is difficult to identify the original key owner when given an exposed key. This leaves the malicious cloud users a chance to leak their access credentials to outsourced data in clouds for profits without the risk of being caught, which severely damages data security. To address this problem, we add the property of traceability to the conventional CP-ABE. To catch people leaking their access credentials to outsourced data in clouds for profits effectively, in this paper, we first propose two kinds of non-interactive commitments for traitor tracing. Then we present a fully secure traceable CP-ABE system for cloud storage service from the proposed commitment. Our proposed commitments for traitor tracing may be of independent interest, as they are both pairing-friendly and homomorphic. We also provide extensive experimental results to confirm the feasibility and efficiency of the proposed solution.

With the booming Internet industry, users' demand for resources is also increasing. In order to meet the needs of users, cloud computing came into being. In today's era, cloud storage is already the most popular application in cloud computing, and users are increasingly inclined to store important and private information in the cloud. One of the most common methods is to encrypt the information before uploading it to the cloud, but searching the information it needs in the cloud server becomes an obstacle, and symmetric searchable encryption can help users achieve this. In this paper, we design a verifiable method based on an efficient symmetric searchable encryption scheme to verify the correctness of search results returned by the cloud server. Through safety analysis and performance evaluation tests, we demonstrate that our solutions are safe and effective.

Searchable encryption is a cryptographic primitive that allows users to search for keywords on encrypted data. It allows users to search in archives stored on cloud servers. Among searchable encryption schemes, those supporting multiuser settings are more suitable for daily application scenarios and more practical. However, since the cloud server is semi-trusted, the result set returned by the server is undefined, and most existing multi-user searchable encryption schemes rely heavily on trusted third parties to manage user permission. To address these problems, verifiable multi-user searchable encryption schemes with dynamic management of user search permissions, weak trust on trusted third parties and are desirable. In this paper, we propose such a scheme. Our scheme manages user permission and key distribution without a trusted third party. User search permission and user access permission matrices are generated separately to manage user permissions dynamically. In addition, our scheme can verify the result set returned by the cloud server. We also show that our scheme is index and trapdoor indistinguishable under chosen keyword attacks in the random oracle model. Finally, a detailed comparison experiment is made by using the actual document data set, and the results show that our scheme is efficient and practical.

With the increasing popularity of publish-subscribe systems in the cloud environment, it is of great value to implement an efficient and secure searchable and encrypted publishing subscription scheme with dynamic attributes in a cloud environment. In a publish-subscribe system, it is very important that the user's attributes can be dynamically changed. Most existing schemes which combine searchable encryption with attribute base encryption do not take real-time changes to user attributes into account or are inefficient. To address these problems, we propose a revocable publish-subscribe scheme (RPS) using CP-ABE with efficient attribute and user revocation capability for cloud systems. RPS supports multiple publishers and subscribers, and protects data from access of unauthorized users and cloud server. In addition, it also protects the privacy of the subscription policy, privacy of the uploaded data and keywords, and is forward and backward secure.

A key challenge to design searchable encryption in multi-user setting lies in the efficient management of encryption and search keys. Existing multi-user searchable encryption schemes either extend the single-user searchable encryption framework with broadcast encryption or require search user refers to the data owner and get the search token. However, this implies the necessity that the data owner distributes a single shared secret key among the group of users or requires the data owner stay online to authorize other users to search. In this paper, we address this practical problem, which is neglected in the literature. We also study secret-key-recovery attack where a malicious user can deduce a valid secret when given a search token. We show such attack violates secret key privacy, which is important in the whole system. Inspired by asymmetric group key agreement and multilinear map technology, we provide a secure and efficient encrypted keyword search scheme for multi-user setting, in which a data owner can share data with a group users without knowing which user in the group. In the proposed scheme, (a) each user has his own secret key, (b) each user generates trapdoors without getting any help from data owner or the third party, (c) our scheme features constant communication overhead, and (d) our scheme resist the secret-key-recovery attack. Our scheme preserves the traceability inherited from the asymmetric group key agreement system. We offer rigorous security proof of our scheme, and the performance analysis demonstrates the efficiency of our scheme.

As a sophisticated mechanism for secure fine-grained access control over encrypted data, ciphertext-policy attribute-based encryption (CP-ABE) is one of the highly promising candidates for cloud computing applications. However, there exist two main long-lasting open problems of CP-ABE that may limit its wide deployment in commercial applications. One is that decryption yields expensive pairing cost which often grows with the increase of access policy size. The other is that one is granted access privilege for unlimited times as long as his attribute set satisfies the access policy of a given ciphertext. Such powerful access rights, which are provided by CP-ABE, may be undesirable in real-world applications (e.g., pay-as-you-use). To address the above drawbacks, in this paper, we propose a new notion called auditable $\sigma $ -time outsourced CP-ABE , which is believed to be applicable to cloud computing. In our notion, expensive pairing operation incurred by decryption is offloaded to cloud and meanwhile, the correctness of the operation can be audited efficiently. Moreover, the notion provides $\sigma $ -time fine-grained access control . The cloud service provider may limit a particular set of users to enjoy access privilege for at most $\sigma $ times within a specified period. As of independent interest, the notion also captures key-leakage resistance . The leakage of a user’s decryption key does not help a malicious third party in decrypting the ciphertexts belonging to the user. We design a concrete construction (satisfying our notion) in the key encapsulation mechanism setting based on Rouselakis and Waters (prime order) CP-ABE, and further present security and extensive experimental analysis to highlight the scalability and efficiency of our construction.

Ciphertext-policy attribute-based encryption, denoted by CP-ABE, extends identity based encryption by taking a set of attributes as users’ public key which enables scalable access control over outsourced data in cloud storage services. However, a decryption key corresponding to an attribute set may be owned by multiple users. Then, malicious users are subjectively willing to share their decryption keys for profits. In addition, the authority who issues decryption keys in CP-ABE system is able to generate arbitrary decryption key for any (including unauthorized) user. Key abuses of both malicious users and the authority have been regarded as one of the major obstacles to deploy CP-ABE system in real-world commercial applications. In this paper, we try to solve these two kinds of key abuses in CP-ABE system, and propose two accountable CP-ABE schemes supporting any LSSS realizable access structures. Two proposed accountable CP-ABE schemes allow any third party (with the help of authorities if necessary) to publicly verify the identity of an exposed decryption key, allow an auditor to publicly audit whether a malicious user or authorities should be responsible for an exposed decryption key, and the key abuser can’t deny it. At last, we prove the two schemes can achieve publicly verifiable traceability and accountability.

The Internet of Things is increasingly becoming a ubiquitous computing service, requiring huge volumes of data storage and processing. Unfortunately, due to the unique characteristics of resource constraints, self-organization, and shortrange communication in IoT, it always resorts to the cloud for outsourced storage and computation, which has brought about a series of new challenging security and privacy threats. In this article, we introduce the architecture and unique security and privacy requirements for the next generation mobile technologies on cloud-based IoT, identify the inappropriateness of most existing work, and address the challenging issues of secure packet forwarding and efficient privacy preserving authentication by proposing new efficient privacy preserving data aggregation without public key homomorphic encryption. Finally, several interesting open problems are suggested with promising ideas to trigger more research efforts in this emerging area.

Cognitive radio network (CRN) enables dynamic spectrum management where spectrum aggregation and sharing have significantly facilitated relieving the supply-demand gap among skyrocketing number of mobile devices. Secondary users (SUs) can collaboratively detect and exploit the available spectrums of primary users (PUs) from different locations. Unfortunately, it has brought a series of security threats leaking user's location privacy to unauthorized entities. The existing work either had privacy breaches or depended on computationally-intensive public key homomorphic encryption loading intolerably high complexity on resource-constrained SUs. To well address these issues, in this paper, a lightweight privacy-preserving spectrum aggregation and auction scheme PPSAS is proposed in CRNs without leveraging public key homomorphic encryption. Firstly, a new primitive of efficient privacy preserving multiparty data aggregation protocol PPMDA is proposed based on any one-way trapdoor permutation. Then based on PPMDA, an efficient privacy preserving spectrum aggregation scheme PPSRA and spectrum auction scheme PPSSA are respectively devised to constitute our proposed PPSAS. Especially, a decentralized PPMDA is extended to resist collusion attack and a secure extended outsourced stable complete marriage matching protocol is given to flexibly realize SU/auctioneer optimization. Finally, formal security proof and extensive simulations demonstrate that our proposed PPSAS well protects the SUs' location privacy against collusion attacks and possesses the advantages over the sate-of-the-art in terms of auctioneer's revenue, SUs' satisfaction, computational and communication overhead.

In our previous work, we explored the possibility of applying machine learning technique to graph partition. We use some metrics to describe the graph, rank the execution time of some graph algorithm and feed them into the machine learning models. We proved that decision tree and KNN and good models of this problem. In the paper, we go on to investigate more metrics to describe the graph after partitioning. We found that AverageDegreeNotCut is also an important metric. We improve the precision score of original machine learning models by 4.9 percent.

In this paper, we propose a revocable public key encryption with authorized keyword search (RPEAKS) scheme. In RPEAKS, data and keywords are encrypted with one public key, users without corresponding private key need authorization from the manager if they want to search through the ciphertexts. The manager authorizes users by distributing authorized tokens to them and is able to revoke a user’s search right when needed. Then we present a concrete RPEAKS construction, which is featured with constant size tokens and efficient revocation. Security analysis shows that the scheme is semantically secure against chosen keyword attack and is trapdoor unforgeability.

Searchable encryption (SE) is a new cryptographic technique that allows data users searching for the files of their interests over huge amounts of encrypted files on the cloud. When it comes to multi-user setting, more issues should be addressed comparing to single-user setting, including key distribution, search privilege control and access control. In this paper, we propose DMU-ABSE, a dynamic multi-user ciphertext-policy attribute-based searchable encryption scheme with file deletion and user revocation. We manipulate an attribute-based encryption to achieve fine-grained search privilege control and hidden policy in multi-user setting while searching time of the proposed scheme is constant (O(1)). With the help of proxy re-encryption, we build one searchable index matrix by different owners in order to improve the searching efficiency. Furthermore, our scheme implements access control by embedding decryption keys into the index matrix. The proposed scheme is proved IND-CKA and IND-CPA semantically secure and experimental results shows that our scheme is efficient.

International audience; A number of searchable encryption schemes have been widely proposed to solve the search problem in ciphertext domain. However, most existing searchable encryption schemes are vulnerable to keyword guessing attacks. During keyword guessing attacks, with the help of the cloud, an adversary will learn what keyword a given trapdoor is searching for, which leads to the disclosure of users’ privacy information. To address this issue, we propose SDKSE-KGA: a secure dynamic keyword searchable encryption scheme which resists keyword guessing attacks. SDKSE-KGA has constant-size indexes and trapdoors and supports functionalities such as dynamic updating of keywords and files. Formal proofs show that it is Trapdoor-IND-CKA and Index-IND-CKA secure in the standard model.

The highly development of Internet and cloud computing technology makes it possible to share computing power, storage and data. Cloud computing makes users with limited computing resources be able to carry out large computational tasks with the help of cloud. but security is a major concern that hinders the widespread use of outsourcing computing. We focus on outsourcing large matrix multiplication in this paper. Firstly, we exploit Chinese Remainder Theorem(CRT) to construct a large matrix multiplication outsourcing protocol which is highly efficient in the malicious cloud model. Then formal security proof and extensive performance evaluation show that our protocol achieves a higher security level i.e., information-theoretic security for input privacy and adaptive chosen ciphertext attack (CCA2) security for output privacy). Compared with previous works, users in the proposed protocol are able to verify the results received from the cloud with tiny error rate.

Cloud computing has an significant impact on both academia and industry with its rapid development. by moving expensive operations like search to cloud, we are able to carry out complicated tasks efficiently. At the same time, cloud security has become a major concern. Malicious clouds are likely to sell privacy information for extra benefit. In order to protect privacy, data are encrypted before being uploaded to cloud. So how to handle encrypted data becomes a new challenge. In this paper we propose SDKSE: a secure dynamic keyword searchable encryption scheme for email systems under cloud computing environment. It is able to update files and keywords at any time. The indexes and the trapdoors generated in the scheme are constant size. In addition, we prove that it is adaptively Trapdoor-IND-CKA and Index-IND-CKA in the standard model.

For the first time, one new conception, multi-proxy multi-signature binding positioning protocol, is proposed. Based on the secure positioning protocol, one model of multi-proxy multi-signature binding positioning protocol is proposed. In the model, positioning protocol is bound to multi-proxy multi-signature tightly, not loosely. Further, we propose one scheme of multi-proxy multi-signature binding positioning protocol, its correctness is proved, and its security is analyzed. Copyright © 2016 John Wiley & Sons, Ltd.

The paper presented an identity based encryption (IBE) under selective opening attack (SOA) whose security is almost-tightly related to a set of computational assumptions. Our result is a combination of Bellare, Waters, and Yilek’s method [TCC, 2011] for constructing (not tightly) SOA secure IBE and Hofheinz, Koch, and Striecks’ technique [PKC, 2015] on building almost-tightly secure IBE in the multi-ciphertext setting. In particular, we first tuned Bellare et al.’s generic construction for SOA secure IBE to show that a one-bit IBE achieving ciphertext indistinguishability under chosen plaintext attack in the multi-ciphertext setting (with one-sided publicly openability) tightly implies a multi-bit IBE secure under selective opening attack. Next, we almost-tightly reduced such a one-bit IBE to static assumptions in the composite-order bilinear groups employing the technique of Hofheinz et al. This yielded the first SOA secure IBE with almost-tight reduction.

Nowaday, secure cloud computation has become more and more attractive. We propose a protocol for privacy-preserving pattern matching. Then, we prove that the construction is simulation-based secure in the semi-honest setting. Finally, the efficiency analysis shows our scheme outperforms the previous works. Specially, compared with Fast' protocol our scheme is more efficient and the text owner does not need real-time online in the query phase. Thus, this protocol is flexible to query by light weight equipments such as mobile phones. Copyright © 2016 John Wiley & Sons, Ltd.

Security and privacy is a prerequisite for the development of mobile networking in big data era. However, security scheme usually demands complex and inefficient implementation. One interesting difference between mobile social networks (MSNs) and web-based social networks is that: user in MSNs is identified by a bunch of serial numbers, while in traditional web-based social network, is identified by arbitrarily strings. Two obvious examples are the phone number in mobile networks and the license number in vehicular ad hoc networks. Taking advantage of this minor difference, we develop a lightweight functional public key encryption scheme and demonstrate its applications for mobile networks. We call such a scheme the Serial Number Based Encryption (SNBE). Our scheme is provable secure base on the DBDH assumption in the standard model, while the ciphertext consists of only three elements.

Attribute-based signcryption (ABSC) can fulfill the functionality of attribute-based signature (ABS) and attribute-based encryption (ABE) in a logical step. Depending on whether the access policy is embedded in the keys or ciphertexts, ABS and ABE are generally divided into two types: key policy and ciphertext policy. In this paper, we propose an ABSC scheme supporting key policy signature and ciphertext policy encryption, called KCP-ABSC, which is the first ABSC scheme with hybrid access policy to our known. The proposed KCP-ABSC scheme is proven to be ciphertext indistinguishability under chosen ciphertext attacks and achieves existential unforgeability under chosen message attack in the standard model. Furthermore, the size of the ciphertext in the new KCP-ABSC scheme is constant, i.e., independent of the number of attributes used in the system.

E-healthcare systems have been increasingly facilitating health condition monitoring, disease modeling and early intervention, and evidence-based medical treatment by medical text mining and image feature extraction. Owing to the resource constraint of wearable mobile devices, it is required to outsource the frequently collected personal health information (PHI) into the cloud. Unfortunately, delegating both storage and computation to the untrusted entity would bring a series of security and privacy issues. The existing work mainly focused on fine-grained privacy-preserving static medical text access and analysis, which can hardly afford the dynamic health condition fluctuation and medical image analysis. In this paper, a secure and efficient privacy-preserving dynamic medical text mining and image feature extraction scheme PPDM in cloud-assisted e-healthcare systems is proposed. Firstly, an efficient privacy-preserving fully homomorphic data aggregation is proposed, which serves the basis for our proposed PPDM. Then, an outsourced disease modeling and early intervention is achieved, respectively by devising an efficient privacy-preserving function correlation matching PPDM1 from dynamic medical text mining and designing a privacy-preserving medical image feature extraction PPDM2. Finally, the formal security proof and extensive performance evaluation demonstrate our proposed PPDM achieves a higher security level (i.e., information-theoretic security for input privacy and adaptive chosen ciphertext attack (CCA2) security for output privacy) in the honest but curious model with optimized efficiency advantage over the state-of-the-art in terms of both computational and communication overhead.

With the emerging techniques of wireless communication and cloud computing, large volumes of multimedia data are outsourced from resource-constrained users to the cloud with abundant resource for both delegated storage and computation. Unfortunately, there exists a risk of users’ image privacy leakage in the process of outsourcing to the cloud in the malicious setting. Most of the existing work achieved privacy-preserving verifiable image feature extraction and matching by using public key (fully) homomorphic encryption (FHE), and the heavy computational overhead and communication overhead cannot adapt to resource-constrained mobile devices. Other works disabled to achieve image denoising in the encrypted domain or only focused on the scale-invariant feature transform (SIFT) descriptor that is inappropriate for position-sensitive feature extraction. To address these issues, in this paper, a privacy-preserving verifiable shape context based image denoising and matching protocol PVIDM with efficient outsourcing in the malicious setting is proposed. To achieve this end, by exploiting any one-way trapdoor permutation, an efficient privacy-preserving verifiable image denoising scheme PPVID is firstly proposed to improve the accuracy of image matching, without exploiting public key FHE. Then, based on PPVID, a lightweight shape context based privacy-preserving verifiable image feature extraction and matching protocol PPVIM is devised, where secure and efficient comparison and counting protocols in the encrypted domain are respectively proposed. Both image privacy and image matching result privacy are well protected, and the correctness of image matching result can be efficiently verified. Finally, formal security proof and extensive simulations on real-world data sets demonstrate the efficiency and practicability of our proposed PVIDM. Especially, both the computational complexity for the usage of any one-way trapdoor permutation at data owner’s/user’s end and the size of the authentication tag generated by the cloud are O(1), independent to both the number of database images and the number of pixels in each image.

Dynamic searchable symmetric encryption (DSSE) is helpful to users with limited storage. In DSSE, the users are able to perform search and update queries on the ciphertext which stored on the cloud server. However, DSSE may suffer from file-injection attacks and leak information after deletion. In order to solve these problems, DSSE schemes with forward security and/or backward security have been proposed. In this paper, we propose two efficient DSSE schemes. The first one supports forward security using binary tree as the basic data structure. The second one uses puncturable encryption to achieve both forward and backward privacy.