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ArticlesAn Efficient and Secure Attribute-Based Online/Offline Signature Scheme for Mobile Crowdsensing
• Hanshu Hong, Bing Hu, and Zhixin Sun*

Human-centric Computing and Information Sciences volume 11, Article number: 26 (2021)
Cite this article 3 Accesses
https://doi.org/10.22967/HCIS.2021.11.026

Abstract

The mobile crowdsensing network has gradually become an important means of realizing comprehensive perception. In the mobile crowdsensing scenario, people use many kinds of smart devices to acquire different types of data in the physical world. Since the terminal devices have limited computation resources and they are vulnerable to various attacks, however, ensuring data security with limited resources is an urgent challenge to be tackled. In this paper, we propose a key policy attribute-based online/offline signature (KP-ABOS) scheme to provide secure and efficient data authentication for mobile crowdsensing. Our KP-ABOS combines the merits of attribute-based cryptography and chameleon hash function, allowing a data owner to set up fine-grained access regulations and conduct pre-calculation in the offline phase. In the online phase, the data owner only needs to perform simple computation tasks; thus sharply reducing the burden of the terminal devices. Based on security proof and performance analysis, Our KP-ABOS has the essential security properties and high efficiency.

Keywords

Security, Signature, Chameleon Hash, Efficiency

Instruction

With the progress of embedded sensors and wireless communication technology, more and more sensors are integrated into various mobile terminals such as smartphones, PC, wearable devices, etc. Under such circumstances, the mobile crowdsensing network formed by a large number of users' portable mobile perception devices through wireless multi-hop communication and Internet of Things (IoT) platform has gradually become an important means of realizing comprehensive perception.
The data acquisition and collection process of mobile crowdsensing is quite different from the traditional sensing network. In the mobile crowdsensing scenario, people use many kinds of smart devices (such as mobile phone, tablet, and glasses) to acquire different types of data (e.g., text and audio) in the physical world. These data are transferred through wireless communication networks (such as Wi-Fi and 5G) and gathered in the cloud in the form of data flow. Scientists can process and analyze these data to obtain a variety of valuable knowledge that can ultimately be applied for the benefit of human society via machine learning and AI techniques.
As shown in Fig. 1, a typical mobile crowdsensing network usually includes four layers: data acquisition layer, data transmission layer, service platform layer, and upper applications layer. Perception participants in the data acquisition layer use various sensors embedded in mobile terminal devices to perceive and collect data. These mobile devices are heterogeneous, such as smartphone cameras, GPS, etc. The perceptual object can be perceived by the participants themselves or the environment where the participants stay. Service consumers can be general users as well as the perceiving participants themselves or other enterprises. Participants then transfer these data to the service platform via multiple channels in the data transmission layer. The service platform is responsible for distributing perceptive tasks, collecting perceptive data, publishing statistical results and supporting upper applications, etc.

Fig. 1. Architecture of mobile crowdsensing network.

A mobile crowdsensing activity usually consists of the following five steps:
Step 1: The mobile crowdsensing service platform publishes the sensing tasks, and users decide whether to participate or not. The service platform may also encourage users to get involved in completing the task via some incentive mechanisms.
Step 2: After accepting the task, participants use various mobile terminal devices to perceive the target data according to the task requirements and store them locally.
Step 3: Participants may periodically or irregularly transfer the collected data to the service platform.
Step 4: When a certain amount of perception data are collected, the service platform will analyze these data to obtain the statistical results and return the results to consumers. For this, either the service platform itself directly publishes statistics to the public, or consumers query the service platform according to their own demands.
Step 5: The service platform evaluates the contribution of participants in terms of the perception data submitted and rewards the participants according to the predefined mechanism.
Depending on the mobile crowdsensing network, users can use their mobile terminal devices to cooperate with each other via various communication techniques to realize the distribution of perception tasks and the analysis of perception data. Due to its properties, the mobile crowdsensing network can be deployed to many scenarios such as intelligent transportation [1], urban management, environmental monitoring, social relations [2], public security, etc.
In the mobile crowdsensing network, a large number of users are required to participate in various applications to collect information and report it. With the growing number of security incidents occurring every year, however, people are increasingly aware of the importance of protecting their personal privacy. The collection of massive individual data will cause people to worry about the disclosure of private information, which will directly threaten people’s property and life security. Consequently, the protection of users’ privacy is a major prerequisite for people to deploy the mobile crowdsensing network to real-world applications.
Goyal et al. [7] proposed the first notion of key policy attribute-based encryption (KP-ABE) in 2006. In this primitive, a user’s private key corresponds to an access structure while target ciphertexts are labeled with a set of attributes. Only when the attributes fit in the access structure can a user decrypt the ciphertext properly. Compared to the conventional public cryptographic mechanism, KP-ABE is more flexible [3] and is appropriate for deployment to application scenarios such as database security protection [4], pay TV access control, etc. Take the pay TV scenario for instance; assuming a consumer purchases the VIP access privileges of “Sports” and “Movie,” then the access structure of his private key can be illustrated in Fig. 2. When a TV program is encrypted with the attribute “Sports,” “VIP,” or “Movie,” the consumer’s private key structure can be fit in; thus, the program can be accessed.
Fig. 2. An illustration of access structure of KP-ABE.

Fig. 3. An illustration of attribute revocation.

When the consumer stops the renewal of his “Sports” VIP access privileges, then the structure of his private key will evolve as shown in Fig. 3. In this scenario, the consumer can still watch the program related to “Movie,” “VIP.” Since the attribute “Sports” had been revoked, however, he can no longer access the program encrypted by “Sports,” “VIP.”
The chameleon hash function is a special cryptographic primitive and has been widely designed for constructing chameleon signature schemes. A chameleon signature scheme is made up of a common digital signature scheme and a chameleon hash scheme by utilizing the hash-then-sign mechanism. In chameleon signature, the verifier of the signature holds the trapdoor information. The signer outputs a hash value of a message and generates a signature for this hash value using the private key he owns. Then the signer delivers the message along with the signature to the verifier. The verifier calculates the chameleon hash value of the message and verifies the legality of the signature using the signer’s public key.
Chen et al. [5] constructed a novel online/offline signature scheme based on the chameleon hash with low calculation burden and key exposure protection property. Inspired by their novel idea, and to solve the security issues better in the mobile crowdsensing scenario, we propose a key policy attribute-based online/offline signature (KP-ABOS) scheme in this paper to provide secure and efficient data authentication. Our scheme combines the merits of attribute-based cryptography [6] and chameleon hash function, allowing a data owner to set up fine-grained access regulations and conduct pre-calculation in the offline phase. The data owner only needs to perform very few computation operations in the online phase, thereby reducing the calculation burden of the terminal devices. Based on security proof and performance analysis, the proposed scheme has the essential security properties and high efficiency.

Related Works

Security in Mobile Crowdsensing
Chatzopoulos et al. [7] applied the blockchain and smart contract technique to the mobile crowdsensing scenario. Their scheme enhanced the privacy protection of terminal users and improved the transaction efficiency at the time. Ni et al. [8] designed a privacy-preserving mobile crowdsensing architecture for location-based applications. Their scheme achieved both the properties of privacy protection and resource allocation balancing at the same time. To solve the security issue of fake sensing injection, Sood et al. [9] applied deep networks to filter out the potential illegal tasks delivered to the mobile crowdsensing center. Their scheme outperformed conventional proposals with respect to precision and recall. Nakayama [10] pointed out that there is a gap in application installation sharing among multiple organizers. To address this issue, the author presented a resource allocation framework for the network side, which reduced the application installation consumption and guaranteed the data integrity of sensors. Mille et al. [11] presented a location privacy-preserving mobile crowdsensing scheme by combining it with the merit of anonymous reputation mechanism. The interaction process between different data collectors and senders were also defined in detail. Arafeh et al. [12] introduced the blockchain technique to present a blockchain-based hybrid architecture for detecting fake sensing activities in mobile crowdsensing. The proposed scheme leveraged the capabilities of the blockchain network and introduced a new role to the mobile crowdsensing architecture to ensure the validation of the collected information. Furthermore, actual experiments were conducted over 200 mobile terminals, with the results demonstrating the effectiveness of the scheme. Ji and Chen [13] studied the collusion resistance issue for incentive mechanisms in crowdsensing applications and announced important related findings. They also proposed solutions that could resist any form of collusion attacks, including even profit trading among the attackers. Krontiris and Dimitriou [14] aimed to solve the problem of information discovery by data consumers and introduced cloud-based agents organized as a decentralized tree structure. Compared to conventional schemes, the proposed mechanism had preferable scalability, efficiency, and manageability. Vakilinia et al. [15] proposed a data aggregation mechanism for the crowdsensing scenario by utilizing linear transformation and homomorphic cryptography. The presented scheme allowed the server to acquire aggregated sensing results without leaking sensitive information. Muniandi [16] designed a blockchain-enabled data collection scheme for mobile crowdsensing and applied it to the railway signaling preparation scenario, and it ensured the trustworthiness and integrity of the business processes. Li et al. [17] proposed a secure data deduplication scheme for crowdsensing services by utilizing certificateless cryptography. Their scheme was able to eliminate the massive duplicated data, satisfying the security requirements of the mobile crowdsensing applications. Zhang et al. [18] pointed out that mobile crowdsensing workers need to report their locations when performing perception tasks, which inevitably caused some sensitive information leak. To solve this issue, they presented a location privacy-preserving scheme for mobile crowdsensing. Their scheme allowed two servers to collaborate to deal with the item queries while ensuring the confidentiality of the item content and workers’ personal details at the same time. In order to achieve balance between privacy and practicability, Xiong et al. [19] designed a lightweight privacy-preserving mechanism for mobile crowdsensing. The theoretical analysis and simulation results demonstrated the security and computational feasibility of the scheme. To solve the problem of impediments during large-scale deployment, Tao et al. [20] proposed a privacy-preserving incentive mechanism for the mobile crowdsensing scenario. The mechanism adopted a trust third party and utilized the primitive of blind signature to protect the privacy of the participants. Furthermore, they designed an incentive method that maximized the benefit of crowdsensing task. For the crowdsensing application, Zhang et al. [21] proposed a truth discovery scheme that introduced little computation overhead by delegating most operations to the cloud server. The users’ private data were also kept confidential during the entire process of truth discovery. Xu et al. [22] designed a fog-assisted crowdsensing architecture for vehicular applications. Their scheme solved the trust assessment issue by converting it into a maximum likelihood estimation problem. The experiments in both simulated environment and real-world mobility traces demonstrated the effectiveness and reliability of their scheme.

Online/Offline Signature
Chen et al. [23] designed an online/offline signature scheme for the people-centric sensing scenario. By separating the signature process into two phases, the efficiency was sharply improved. Ming and Wang [24] optimized an identity-based online/offline signature scheme and overcame the drawback of universal forgery. Their scheme was also resistant to the chosen message attack under a computational Diffie-Hellman (CDH) hardness assumption. Liu et al. [25] first introduced the notion of online/offline ring signature scheme with high calculation efficiency and which was appropriate for deployment in the mobile device scenario. The concrete algorithms along with strict security definitions were also presented. To tackle the authentication issues in VANETs, Li et al. [26] designed an online/offline certificateless aggregate signature. Their scheme not only avoided the maintenance overhead associated with PKI but also handled the key escrow problem in ID-based cryptography properly. Moreover, the algorithms eliminated the pairing and hash to map operations to improve the overall efficiency of the proposed scheme. Li et al. [27] also pointed out that the point multiplication operation in the online phase would be an obstacle to applying signature schemes in computation resource-constrained devices. To make up for this drawback, they presented a novel online/offline signcryption scheme and performed security analysis in a random oracle model. Chen et al. [28] extended the application scenario of online/offline signature in a multiple-signer setting. Their scheme achieved constant communication overhead and required only three times of pairing operations when verifying a valid signature. Zheng et al. [29] proposed a novel lattice-based online/offline signature scheme by utilizing the hash-sign-switch paradigm that achieved quantum attack resistance.

Models and Definitions

Model and Architecture
The architecture of our scheme is illustrated in Fig. 4. It includes three entities: authority, terminal devices, and service platform. Authority is responsible for generating system parameters and distributing them to both service platform and terminal devices via secure channels. Terminal devices perform perception tasks and generate signatures over the data to be transferred. Service platform distributes different perception tasks to terminal devices and verifies if the received signature is a valid one. Note that the perception data are labeled by several attribute tags, and the signature can be verified based on the access structure of a terminal user.
Fig. 4. Architecture of the proposed scheme.

Table 1. Notations and meanings
Notations Meanings
PK Public parameters
𝑆𝐾 Private parameters
A_i Single attribute
𝛾 Access structure
$sk_γ$ Private key for γ
𝑟 Integer
m Message
Chameleon hash value
f() Hash function
𝐶𝑇 Ciphertext
V Signature
PK Public parameters
SK Private parameters

Syntax
Before presenting our scheme, some notations are defined in Table 1 to ensure clarity of description.
Setup: This algorithm generates system essential parameters PK,SK and broadcasts PK.
Key generation: This algorithm generates private key $sk_γ$ for a user holding access structure γ.
Sign: This algorithm can be divided into two phases: offline phase and offline phase.
Offline phase: Upon input of random message m and integer r, it outputs original signature V and stores the pair (m,r).
Online phase: For a new given message $m_j$, it calculates the corresponding pair ($m_j$,$r_j$ ).
Verify: This algorithm verifies if V is a valid signature.

Security Definitions
Collision resistance: Without the hash key, no polynomial algorithm can generate two pairs of (m,r),(m',r'), satisfying (1) with non-negligible probability:

(1)

Unforgeability: The essential unforgeability of our scheme can be defined via a security game participated in by Challenger and Adversary, which are defined as follows:
Setup:Challengerruns the Setup procedure to obtain the system parameters and sends PK to Adversary.
Key generation queries: Adversary can require private key generation and access structure γ.Challenger runs the Key generation algorithm and sends the result to Adversary.
Sign queries: Adversary chooses access structure γ and plaintext M and makes Sign queries to Challenger. Challenger runs the Sign algorithm and returns the result to Adversary.
Challenge: Adversary claims the challenging $γ_c$ ,$m_c$ that have not been queried previously and outputs challenging signature $V_c$. Challenger verifies $V_c$ by running the Verify algorithm, and Adversary wins if the output of the Verify algorithm is valid.

Hardness Assumption
Discrete logarithm problem (DL): For a∈$Z_q^*$, given (𝑔,$g^a$), no probabilistic polynomial-time (PPT) algorithm can calculate the value of a with non-negligible probability.

Constructions

Concrete Algorithms
Setup: Defines a q-order group $G_1$; let p be a generator of $G_1$.
Picks secret number y∈$Z_q^*$ and computes Y=yp.
Picks random numbers $t_i$∈$Z_q^*$ for each attribute in the system and computes $T_i$=$t_i$ p.
Sets hash function $f:{0,1}^*$→∈$Z_q^*$.
The system public parameters are PK={$G_1$,p,q,$A_i$,Y,$T_i$,f}.
Key generation: For a user with access structure γ($A_i$∈γ), randomly chooses polynomial $q_x$ for each node x in the user’s access tree γ. Denote $d_x$ as the degree of $q_x$ and $thr_x$ as the threshold value node. Let $d_x=thr_x-1$. For the root node, sets $q_{root}$ (0)=y. For any other node (except for root node) in the access tree, let $q_x$ (0)=$q_{parent(x)}$ index(x). The private key is set to be $sk_γ$={$q_x$ (0)+$t_i$,$A_i$∈γ}.
Sign: The data owner randomly picks k∈$Z_q^*$; let K=kp. For random message m and integer r, the signer first defines a chameleon hash in (2):

(2)

Offline phase: Picks secret number s∈$Z_q^*$ and computes:

(3)

Online phase: For a given message $m_j$, computes $r_j$ in (4):

(4)

Then the signature can be defined as V={$v_i$,S,($m_j$,$r_j$ ),K}.
Verify: The platform verifies the signature by calculating (5) and verifies if (6) holds:

(5)

(6)

Correctness Proof
The correctness of Verify is presented as follows:
First, obtains $v_i∙p-S∙f(h')$ in (7):

(7)

Then calculates $h'$ in (8):

(8)

Through (7) and (8), equation (6) can be proven in (9):

(9)

Performance Evaluation

Collision Resistance Analysis
Theorem 1. If there is a polynomial algorithm that can generate two pairs of (m,r),($m'$,$r'$) without the hash key and which satisfies {$m≠m'$,Hash(m,r)=Hash(m',r')}, then an Adversary can be constructed to solve the DL problem.
Proof: Assume that Adversary does not possess $sk_γ$={$q_x$ (0)+$t_i$,$A_i$∈γ} and k. Its aim is to generate Hash(m,r)=Hash($m'$,$r'$) by forging a new pair of ($m'$,$r'$). From the aforementioned description, if Hash(m,r)=Hash($m'$,$r'$), then (10) holds:

(10)

Thus, equation (11) below also holds:

(11)

Let $a=k∙(∑_{A_i∈γ}(q_x (0)+t_i ) )^{-1}$, then $ap=(∑_{A_i∈γ}(q_x (0)+t_i ) )^{-1}∙K$.
If Adversary successfully forges a chameleon hash value, then it outputs (12) as the solution of the DL problem.

(12)

Unforgeability Proof
Theorem 2. If there is a polynomial algorithm that can forge a valid signature successfully, then an Adversary can be constructed to solve the DL problem.
Proof: Setup: Challenger runs the Setup procedure to generate the system parameters and sends PK={$G_1$,p,q,$A_i$,Y,$T_i$,f} to Adversary. Note that the generation process of each parameter strictly follows the steps of the Setup algorithm defined in the previous section.
Key generation queries: Challenger runs the Key generation procedure and returns the result to Adversary when Adversary requests the private key for certain access structures. Note that the generation process of each parameter strictly follows the steps of the Key generation algorithm defined in Section 4, and the format of a private key is defined as $sk_γ$={$q_x$ (0)+$t_i$,$A_i$∈γ}.
Sign queries: Challenger runs the Sign procedure and returns the result to Adversary when Adversary requests the signature for message m. Note that the generation process of each parameter strictly follows the steps of the Sign algorithm defined in section 4, and the format of a signature is defined as V:{v,S,(m,r),K}.
Challenge:Adversary claims the challenging $γ_c$ ($A_c$∈$γ_c$) ,m_c that have not been queried previously and outputs challenging signature V_c={$v_c$,$S_c$,($m_c$,$r_c$ ),$K_c$}.
If $V_c$ is a valid signature, then the following equation holds:

(13)

Let a=$s_c$ implicitly by setting $S_c$=ap. Then the DL problem can be solved by calculating (14):

(14)

Efficiency Analysis
In this section, we will discuss the efficiency with regard to offline phase and online phase. From the aforementioned description, to execute a signature algorithm, the offline phase requires running 2 conventional hash operations, 4n+1 addition operations, and 5 multiplications (n is the number of attributes constituting it). The online phase requires running 1 conventional hash operation, 2 addition operations, and 1 multiplication. The amount of computation increases in the offline phase as the number of attributes grows but remains constant in the online phase. Consequently, most of the calculation task has been completed during the offline phase, thereby relieving the resource-constrained devices of the heavy online computation burden.

Conclusion

This study proposed a KP-ABOS scheme to provide secure and efficient data authentication for mobile crowdsensing. The scheme combines the merits of attribute-based cryptography and chameleon hash function, achieving the essential security properties along with high efficiency.
In our future research, we will continue to focus on the security concerns in mobile crowdsensing and attempt to design more cryptographic schemes. Moreover, we will combine the mobile crowdsensing scenarios with more advanced techniques such as blockchain [3032], machine learning [33], cloud computing [34], etc.

Acknowledgment

None.

Authors’ contributions

Conceptualization, HH. Funding acquisition, HH, ZS. Writing of the original draft, HH. Writing of the review and editing, HH, BH. Software, HH, BH. Validation, BH. Formal Analysis, HH ZS. All the authors have proofread the final version.

Funding

This research is supported by the National Natural Science Foundation of China (No. 61672299 and 61802200) and Natural Science Foundation of Jiangsu Province (No. BK20180745).

Competing Interests

The authors declare that they have no competing interests.

Author Biography

Name : Hanshu Hong
Affiliation : Nanjing University of Posts and Telecommunications, School of Modern Posts, Nanjing, China
Biography : Hanshu Hong received the B.E. and Ph.D. degrees from the Nanjing University of Posts and Telecommunications, Nanjing, China, in 2013, and 2018, respectively. He is currently a lecture of the School of Modern Posts, Nanjing University of Posts and Telecommunications. His research interests include information security, cryptography.

Name : Bing Hu
Affiliation : Nanjing University of Posts and Telecommunications, School of Modern Posts, Nanjing, China
Biography : Bing Hu received the Ph. D. degree in information networks at Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, China, in 2017. Since 2018, she works in NJUPT and is currently a lecturer of College of Modern Posts. Her research interests are in the areas of wireless sensor network, wireless communications, and mobile edge computing and unmanned aerial vehicles communications.

Name : Zhixin Sun
Affiliation : Nanjing University of Posts and Telecommunications, School of Modern Posts, Nanjing, China
Biography : Zhixin Sun received the Ph.D. degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 1998. From 2001 to 2002, he held a postdoctoral position with the School of Engineering, Seoul National University, South Korea. He is currently a Professor and the Dean of the School of Modern Posts, Nanjing University of Posts and Telecommunications. His research interests are in cloud computing, cryptography and traffic identification.

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Hanshu Hong, Bing Hu, and Zhixin Sun*, An Efficient and Secure Attribute-Based Online/Offline Signature Scheme for Mobile Crowdsensing, Article number: 11:26 (2021) Cite this article 3 Accesses

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• Recived4 February 2021
• Accepted2 June 2021
• Published30 June 2021
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