Security and Communication Networks

Security and Communication Networks / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 8827364 | https://doi.org/10.1155/2020/8827364

Juan Zhang, Changsheng Wan, Chunyu Zhang, Xiaojun Guo, Yongyong Chen, "Noninteractive Lightweight Privacy-Preserving Auditing on Images in Mobile Crowdsourcing Networks", Security and Communication Networks, vol. 2020, Article ID 8827364, 11 pages, 2020. https://doi.org/10.1155/2020/8827364

Noninteractive Lightweight Privacy-Preserving Auditing on Images in Mobile Crowdsourcing Networks

Academic Editor: Clemente Galdi
Received17 Apr 2020
Revised06 Jul 2020
Accepted10 Jul 2020
Published25 Jul 2020

Abstract

To determine whether images on the crowdsourcing server meet the mobile user’s requirement, an auditing protocol is desired to check these images. However, before paying for images, the mobile user typically cannot download them for checking. Moreover, since mobiles are usually low-power devices and the crowdsourcing server has to handle a large number of mobile users, the auditing protocol should be lightweight. To address the above security and efficiency issues, we propose a novel noninteractive lightweight privacy-preserving auditing protocol on images in mobile crowdsourcing networks, called NLPAS. Since NLPAS allows the mobile user to check images on the crowdsourcing server without downloading them, the newly designed protocol can provide privacy protection for these images. At the same time, NLPAS uses the binary convolutional neural network for extracting features from images and designs a novel privacy-preserving Hamming distance computation algorithm for determining whether these images on the crowdsourcing server meet the mobile user’s requirement. Since these two techniques are both lightweight, NLPAS can audit images on the crowdsourcing server in a privacy-preserving manner while still enjoying high efficiency. Experimental results show that NLPAS is feasible for real-world applications.

1. Introduction

Recently, mobile crowdsourcing systems have been widely deployed all over the world, which collect and process data through widely available mobile devices [1]. As discussed in [2], since data typically originate from third parties, falsified data may be reported to the crowdsourcing server. Therefore, to ensure data trust, it is important to check whether the data collected by workers meet the mobile user’s requirement before using it [2]. We call this sort of protocol the auditing protocol. At the same time, to avoid economic loss, mobile users may not be allowed to download images before paying for them. Therefore, to check images before downloading them, the auditing protocol should have the noninteractive feature. Moreover, as shown in [2], data uploaded by participants may contain their private and sensitive information. Therefore, the auditing protocol should have the privacy-preserving feature. Finally, due to limited resources of mobile users, the auditing protocol should be quite efficient. Therefore, a noninteractive lightweight privacy-preserving auditing scheme is needed for the mobile user to check images before downloading them.

Regardless of the technology implemented, a typical “noninteractive lightweight privacy-preserving auditing system (NLPAS)” includes three entities: the “crowdsourcing server (CS)” which stores images, the “mobile user (MU)” who audits images stored on the crowdsourcing server before downloading them, and the worker who collects images and uploads them to the CS. In practice, these entities are involved in two processes (i.e., the uploading process and the auditing process). During the uploading process, the worker collects images and uploads them to the CS. During the auditing process, the MU audits images stored on the CS and then determines whether to download them.

Security has vital significance for NLPAS. To avoid economic loss, the CS is not willing to transport images to the MU before the latter pays for them. On the other hand, the MU is not willing to pay for images before he/she can make sure that these images really meet the requirement. To handle this dilemma, it is reasonable to design a privacy-preserving auditing protocol, which allows the MU to check whether these images meet the requirement before downloading them. Unfortunately, the current security protocols for crowdsourcing systems (i.e., [340]) only consider how the CS checks images uploaded by workers. This leads to two issues. First, to earn more money, the CS is not willing to check images and provide true information to the MU. Second, the requirements of multiple MUs may vary, and the CS may not know them. For example, one MU may be interested in the hill in the image, while another MU may be interested in the lion in the image. In this case, it is impossible for the CS to know the requirements of multiple MUs. So, to make sure the images on the CS meet the requirements of the MU, it is desired to design a privacy-preserving auditing protocol for mobile crowdsourcing systems.

Efficiency is another serious concern for NLPAS. Due to limited resources of mobile devices, the MU is seriously concerned about the high computation cost arising from running the auditing protocol. At the same time, the CS will have to handle a lot of auditing requests from multiple MUs, and it is seriously concerned about the computation cost too. So, the newly designed auditing protocol should be lightweight. Taking both security and efficiency into account, we aim to design a noninteractive privacy-preserving lightweight auditing protocol on images in the mobile crowdsourcing system, which extracts features from images and then determines whether these features meet the MU’s requirement. An auditing protocol for the mobile crowdsourcing system should fulfill the following requirements:(1)Privacy Preserving. When auditing images on the CS, the MU must be ensured that his/her requirement is not leaked to the CS or a third-party adversary. Otherwise, the CS may forge wrong information to pass the auditing, resulting in economic loss of the MU. At the same time, the CS must be ensured that the features of images are not leaked to the MU or a third-party adversary too. Otherwise, it will result in economic loss of the CS.(2)Content Privacy. During auditing, the CS should be ensured that the MU or a third-party adversary cannot extract content of images from exchanged information. Otherwise, the adversary may steal content of images stored on the CS, resulting in economic loss of the CS.(3)Auditing. After the auditing process, the MU should be able to determine whether the content in an image meets his/her requirement.(4)Computation Cost. The CS and the MU should be ensured that their computation costs are low when running auditing algorithms.(5)Accuracy. The CS and the MU should be ensured that the accuracy of the model used for extracting features from images during auditing is high.

Obviously, designing an auditing protocol for NLPAS is a nontrivial task, as the MU has to determine whether the images stored on the CS meet the requirement without downloading them. Recently, security protocols for mobile crowdsourcing systems have focused on image-checking techniques run by the CS. However, there is no protocol considering the image-checking technique established by the MU. Moreover, when focusing on this topic, we notice that there is no security scheme which can be directly used for satisfying all the above requirements. We will present the detailed analysis for arriving at this conclusion in the next section. This becomes a more serious issue, since more and more mobile crowdsourcing systems are being deployed. Motivated by this observation, in this paper, we mainly make three contributions:(1)We present a comprehensive set of requirements for auditing protocols in mobile crowdsourcing systems and show some security and efficiency problems of current data checking protocols in mobile crowdsourcing systems.(2)We propose a novel auditing protocol called NLPAS, which can check whether the images stored on the CS meet the MU’s requirement. However, different from current data checking protocols for mobile crowdsourcing systems, NLPAS allows the MU instead of the CS to check images in a privacy-preserving manner. By doing so, the MU can determine whether the images meet the requirement before paying for them. At the same time, the CS can make sure that those images will not be leaked. To fulfill all these requirements, we will first introduce the “binary convolutional neural network (BCNN)” technique [41] to the newly designed auditing protocol for extracting features of images, and then we will design a novel privacy-preserving Hamming distance computation [42] technique for determining whether the images meet the MU’s requirements. Since these two techniques are quite lightweight, the computation cost of NLPAS can be reduced significantly.(3)We analyze the security of NLPAS, showing it satisfies requirements (1), (2), and (3) in Section 1. And, we evaluate the efficiency of NLPAS, showing it satisfies requirements (4) and (5) in Section 1. We organize the remainder of this paper as follows. First, we investigate the related work in Section 2. Second, we describe the NLPAS protocol in Section 3, followed by security analysis and efficiency evaluation in Sections 4 and 5, respectively. Finally, in Section 6, we draw our conclusions.

2.1. Data Trust

Data trust is an essential security problem in mobile crowdsourcing systems. Due to openness, workers in mobile crowdsourcing systems may have different security abilities, resulting in low data trust [3]. Recently, a lot of papers have been published, focusing on data trust in crowdsourcing systems, as illustrated below.

2.1.1. Voting-Based Data Checking

This sort of scheme takes observed results with the most observers as the true data [46]. For example, in [4], the authors allowed a number of observers to report their evaluation results on data and took the one as true data if most of the evaluation results were positive. Similarly, the authors in [5] aimed to find conflicts of data obtained from multiple workers, which could be seen as a variation of the voting-based data checking scheme.

2.1.2. Context Information-Based Data Checking

This sort of scheme uses context information such as location information to determine whether the data are true or not. For example, in [7], the authors required workers to upload location information together with data, while the CS used location information to determine whether the data were true or not. Similarly, the authors in [8, 9] used the worker’s trajectory for determining whether the data were true or not.

2.1.3. Statistics-Based Data Checking

This sort of scheme [1013] uses statistic methods for evaluating whether the data are true or not. For example, the authors in [10] used maximum likelihood estimation for checking data. And, the authors in [11] used maximum a posteriori (MAP) estimation for determining whether the data were true or not.

2.1.4. Gold Data Set-Based Data Checking

This sort of scheme uses a gold data set for checking data uploaded by workers [14, 15]. For example, in [15], the authors assigned fully trusted workers with a gold data set for checking data uploaded from other workers.

2.1.5. Data Redundancy Checking

This sort of scheme aims to address data redundancy issues [16, 17]. For example, in [16], the authors designed a scheme to find redundancy data from multiple workers and used redundancy data for estimating missing values.

From the above analysis, it can be seen that the existing schemes mainly focus on data checking performed by the CS and workers. And, they mainly consider whether the data are correct. However, existing schemes did not allow the mobile users to check data before downloading it. Moreover, existing schemes only focus on the correctness of data and do not consider whether the data match the MU’s requirement since requirements from multiple mobile users may vary. This leads to a serious issue: The mobile user may waste money on nonmatched data. Therefore, an auditing scheme performed by the MU before data downloading is desired.

2.2. Data Privacy

Data privacy is another important problem in mobile crowdsourcing systems. First, if the data uploaded by workers are leaked, the CS and workers may lose money. More importantly, if the leaked data contain privacy of workers, the adversary may cause them harm. Second, if the data of mobile users are leaked, the adversary may deduce valuable information about mobile users [3]. Therefore, data privacy is a serious concern in crowdsourcing systems. Papers about data privacy in mobile crowdsourcing systems are illustrated below.

2.2.1. Encryption

This sort of scheme aims to encrypt data before uploading them [1821]. For example, in [18], a homomorphic identity-based encryption algorithm was designed for protecting data uploaded by workers.

2.2.2. Differential Privacy

This sort of scheme adds perturbation to data [2225]. For example, in [25], the authors added random perturbation to data uploaded by workers.

2.2.3. Location Privacy

This sort of scheme aims to protect location information of workers [2631]. For example, in [26], the authors used k-anonymity for providing location privacy.

2.2.4. Personal Information Privacy

This sort of scheme aims to protect personal information of workers and mobile users [3235]. For example, in [35], the authors defined multiple privacy level of personal information.

Recently, several new techniques such as blockchain and fog computing have been introduced to mobile crowdsourcing networks [3640]. For example, in [36], the authors used blockchain for user authorization. And, in [38], the authors used fog computing for data aggregation and task allocation. Finally, the features of existing schemes are listed in Table 1.


Data trustPrivacy preservingNoninteractive

[46]Voting-based data checking
[79]Context information-based data checking
[1013]Statistics-based data checking
[14, 15]Gold data set-based data checking
[16, 17]Data redundancy checking
[1821]Encryption
[2225]Differential privacy
[2631]Location privacy
[3235]Personal information privacy
[36]Blockchain-based authorization
[37]Blockchain-based data privacy
[38]Fog-based data privacy

From Table 1, it can be seen that the existing schemes can provide data trust and privacy protection in various ways. However, most schemes only consider one feature, either data trust or privacy preserving. And, no scheme provides both features. Moreover, all existing schemes do not support the noninteractive feature. This leads to a dilemma: on the one hand, to provide privacy protection, the MU cannot get data before paying for it; on the other hand, the MU has to check data before paying for it, to determine whether the data meet the requirement.

Furthermore, for images, this dilemma becomes more serious since the data volume of images is much larger than that of traditional texts. To handle this dilemma, it is desired to design a noninteractive lightweight privacy-preserving auditing protocol on images in mobile crowdsourcing networks, which allows the MU to efficiently determine whether the images meet the requirements without knowing anything about these images.

3. NLPAS: The Protocol

3.1. Preliminaries
3.1.1. Binary Convolutional Neural Networks

A convolutional neural network [41] is a kind of neural networks whose forward propagation operation can be expressed as , where is the output tensor of the operation, is a nonlinear function, and are the weight tensor and the activation tensor generated by the previous neural network layer, and is the convolution operation. A binary convolutional neural network is a kind of convolutional neural networks, whose weights and activations are 1 bit instead of the floating point. And, the forward propagation operation can be expressed as , where and are integers, and each is computed from the corresponding as follows:

Similarly, each is computed from the corresponding as follows:

For bitwise and , the operation can be efficiently computed using the XNOR-bitcount algorithm defined in [43].

Moreover, since the function is not differentiable during backward propagation, Bengio et al. used the function instead of as follows: [44]. By using the function, the binary convolutional neural network can use the same gradient descent algorithm as that of traditional neural networks to update parameters during training.

3.1.2. Hamming Distance

The Hamming distance [42] is defined below.

Given two -bit binary vectors and , where and , the Hamming distance of and is defined as

The above definition shows that the Hamming distance is the total number of different bits between and . In other words, to compute the Hamming distance, we need to count the different bits between and .

3.2. System Model

The main purpose of NLPAS is to determine whether the image on the CS meets the requirement of the MU in a privacy-preserving manner.

The main idea of NLPAS can be divided into two parts, namely, the feature extracting part and the Hamming distance computation part. For the feature extracting part, the MU first defines a binary convolutional neural network and trains it using a data set according to the user’s requirement. Then, the MU extracts a binary vector () from a template image using this binary convolutional neural network, which is used as the requirement feature. Finally, the MU sends the trained binary convolutional neural network to the CS, and the latter extracts a binary vector () from the image to be audited using this trained network, which is used as the auditing feature. Since the binary convolutional neural network is quite lightweight, NLPAS can achieve high efficiency.

For the Hamming distance computation part, the MU and the CS hide the two input vectors ( and ) in a carrier number, respectively. And then, all operations for counting different bits between these two vectors are based on five basic mathematical operations, namely, addition, subtraction, multiplication, division, and modulo operations. Since these five basic mathematical operations are quite lightweight, our scheme can achieve high efficiency.

Based on the feature extracting and Hamming distance computation techniques, the MU compares the Hamming distance of and with a threshold to determine whether the interested image on the CS meets the requirement.

The system model of NLPAS is shown in Figure 1, which includes three phases as described below. And, the notations are listed in Table 2.


NotationDescription

Binary convolutional neural network model trained by the MU
Private parameters of the MU
Public parameters of NLPAS
Feature vector of the MU
Feature vector of the CS
Length of the two binary vectors and
Security strength of NLPAS
Ciphertexts generated by the MU
The first set of random numbers that is hidden in
The second set of random numbers that is hidden in
Ciphertexts generated by the CS
Hamming distance of and
The first set of random numbers that is hidden in
The second set of random numbers that is hidden in
Threshold for determining whether the image on the CS meets the MU’s requirement
Prime number for counting different bits in and
Prime number used as a carrier
The first set of random numbers for hiding
The second set of random numbers for hiding
The third set of random numbers for hiding
The fourth set of random numbers for hiding
The first set of bases for hiding vectors
The second set of bases for hiding vectors
Transitional values for extracting the hamming distance
Values that contain the hamming distance

3.2.1. The Initialization Phase

During this phase, the MU defines a binary convolutional neural network model (), trains it, and extracts a binary vector from the template image using the. Then, the MU generates public and private cryptographic parameters for the NLPAS system. These cryptographic parameters will be used for hiding vectors and extracting results in the following hiding phase and extracting phase. The initialization algorithm is described as follows.

. This algorithm is run by the MU for generating system parameters for NLPAS. It takes as input the length of input vectors (i.e., ) and the security strength of NLPAS (i.e., -bit) and outputs the set of private and public cryptographic parameters (i.e., and ).

Then, the MU sends the public parameter () and the trained model () to the CS. And, the latter extracts a binary vector from the interested image stored on it.

After the initialization phase, the MU holds () and the CS holds .

3.2.2. The Hiding Phase

When the MU wants to compute the Hamming distance where is known only by the MU and is known only by the CS, it establishes the hiding process, by running the algorithm and sending the results to the CS. The algorithm is described below.

. This algorithm is run by the MU for hiding the binary vector into a ciphertext. It takes as inputs the MU’s binary vector (i.e., ), the MU’s private parameter (i.e., ), and the MU’s public parameter (i.e., ) and outputs the ciphertext (i.e., ).

Upon receiving the ciphertext (i.e., ), the CS injects its vector (i.e., ) into using the algorithm and gets the updated ciphertext . Then, the CS sends back to the MU, and the Hamming distance of vectors and is included in . The algorithm is described below.

. This algorithm is run by the CS for injecting the binary vector into . It takes as inputs the CS’s binary vector (i.e., ), the MU’s ciphertext (i.e., ), and the MU’s public parameter (i.e., ) and outputs the updated ciphertext (i.e., ).

After the hiding phase, the MU gets , and the Hamming distance of and is hidden in for being extracted in the following extracting phase.

3.2.3. The Extracting Phase

After receiving the updated ciphertext (i.e., ) from the CS, the MU extracts the Hamming distance from using the algorithm, which is described below.

. This algorithm is run by the MU for extracting the Hamming distance from the updated ciphertext (i.e., ). It takes as inputs the updated ciphertext , the MU’s private parameter (i.e., ), and the MU’s public parameter (i.e., ) and outputs the Hamming distance of and (i.e., ).

After the extracting phase, the MU gets the Hamming distance of and (i.e., ). Then, the MU sets a threshold value . If , the interested image stored on the CS does not match the MU’s requirement. Otherwise, the interested image on the CS matches the MU’s requirement.

In the above system model, the MU’s vector is hidden in using the algorithm, which cannot be known by the CS. At the same time, the CS’s vector is hidden in using the algorithm, which cannot be known by the MU. Therefore, NLPAS can achieve the privacy-preserving goal described in Section 1.

In the above system model, the CS’s vector is hidden in using the algorithm, which cannot be known by the MU. Therefore, the MU only knows the Hamming distance between and and does not know and the corresponding interested image. Therefore, NLPAS can achieve the content privacy goal described in Section 1.

3.3. Construction

The construction of NLPAS is a tuple (, , , ) of probabilistic polynomial time algorithms as shown in Figure 2, and the details are defined below.

. The MU runs this algorithm for generating system parameters for NLPAS as follows. First, the MU generates a -bit prime number for counting different bits. Second, the MU generates a large prime number with the length as the carrier of NLPAS. Third, the MU generates four sets of positive random numbers for hiding , namely, , , , and , where , . Fourth, the MU computes two sets of bases for hiding vectors as follows: and . Finally, the MU gets and .

. The MU runs this algorithm for hiding the binary vector into a ciphertext as follows. The MU computes and for each in . Otherwise, and . Then, the MU gets .

. The CS runs this algorithm for injecting the binary vector into as follows. First, the CS computes and for each in . Otherwise, and . Second, the CS computes and . Finally, the CS gets .

. The MU runs this algorithm for extracting the Hamming distance from the updated ciphertext (i.e., ) as follows. First, the MU computes and . Second, the MU computes and . Third, the MU computes .

In the above construction, NLPAS uses only a few simple mathematical operations (i.e., addition, subtraction, multiplication, division, and modulo operations) instead of time-consuming cryptographic operations such as modular exponentiation. Therefore, it enjoys high efficiency. We will further evaluate the efficiency of NLPAS in Section 5.

4. Security Analysis

In this section, we first show that NLPAS is correct and then analyze the security of NLPAS according to the security requirements described in Section 1 (i.e., privacy preserving, content privacy, and auditing).

4.1. Correctness

In the construction in Section 3.3, we use for counting the bits where and . Similarly, we use for counting the bits where and . Therefore, the Hamming distance of and can be computed as .

In this section, we shall show that can really be used for counting the bits where and . And, the meaning of can be analyzed in a similar way.

We start analyzing the meaning of from the variable as follows. First, according to the algorithm, can be written as

Second, taking the value of in the algorithm into consideration, can be further written as

Third, taking the value of in the algorithm into consideration, can be written as

Fourth, considering all the four conditions (i.e., , , , and ) together, we can compute

Fifth, since the length of is -bit, the length of should be no more than . Since , the length of should be no more than , and the length of should be no more than . Therefore, the length of  +  +  should be no more than . That is to say,  +  + . So, we get

Sixth, since the length of is no more than and the length of is no more than , the length of  +  is no more than . That is to say,  + . So, we get  =  + .

Finally, we get

This is really the total number of bits where and . Similarly, we can learn that is the total number of bits where and . Therefore, the Hamming distance of and is .

From the above discussion, we can see that the main idea of privacy-preserving Hamming distance computation includes two points. First, we hide the information of and in a big prime . Second, the different bits (e.g., and ) are counted in an independent part of , e.g., , which can be extracted using several modulo operations.

4.2. Privacy Preserving

The privacy-preserving requirement is to ensure that the adversary cannot extract or transmitted in the hiding phase.

We first consider the privacy-preserving requirement for , where the adversary can be anybody who is able to get including the CS.

From the algorithm defined in Section 3.3, it can be seen that is hidden in and . Since , , , , and are random numbers known only by the MU, and are random numbers too.

Moreover, since the length of is much longer than , , and , the lengths of and are determined by and . Since and are random numbers, the lengths of and are randomly determined by and , regardless of the value of (i.e., 0 or 1). Therefore, for (i.e., ) and (i.e., ), it may be . Similarly, for and , it may be . That is to say, the set of and including may or may not be bigger than the set of and without . So, the adversary cannot get the value of from and by determining that a bigger random represents 1, or a smaller random represents 1. In other words, without knowing the set of random secrets , the adversary can extract from or only with a negligible probability.

Furthermore, if the length of is , the probability that the adversary can get is .

We then consider the privacy-preserving requirement for , where the adversary can be anybody who is able to get including the MU.

From the algorithm defined in Section 3.3, it can be seen that is hidden in and using the addition operations. Knowing the result of addition, the adversary can extract only with a negligible probability. Therefore, the privacy of is ensured by the addition operation.

Moreover, assuming the MU is the adversary who wants to extract from , the MU has to solve the two equations  +  +  +  and  +  +  + . Since the MU knows , these two equations can be treated as two linear equations with unknown numbers (i.e., ). Therefore, when , there are a number of solutions for them. In addition, for -bit , the number of solutions is . That is to say, the probability that the MU can extract from is .

From the above discussion, it can be seen that the adversary cannot extract or transmitted in the hiding phase. Therefore, NLPAS can achieve the privacy-preserving goal.

4.3. Content Privacy

The content privacy requirement is to ensure that the adversary cannot extract content of the interested image stored on the CS from . The content of the interested image is included in . Since the adversary cannot extract from as shown in the previous subsection, the content of the interested image stored on the CS cannot be extracted. Therefore, NLPAS can achieve the content privacy goal.

4.4. Auditing

The auditing requirement is to ensure that the MU can determine whether the content in the image stored on the CS meets the MU’s requirement. This is ensured by the Hamming distance. If the Hamming distance of and is smaller than the threshold, the MU can determine that the content in the image is the one he/she needs. Otherwise, the content in the image is not needed by the MU. Moreover, the correctness of Hamming distance computation is ensured in Section 4.1.

5. Efficiency Evaluation

As shown in Section 3, NLPAS includes two parts: feature extracting using the binary convolutional neural network and similarity computation using the privacy-preserving Hamming distance. Therefore, we mainly evaluate the computation costs consumed in these two parts. Moreover, for the feature extracting part, we will evaluate the accuracy of the trained model (i.e., ).

5.1. Accuracy

To provide a benchmark of efficiency evaluation, we used the MNIST data set [45] and LeNet [46] for comparing the accuracy of the binary convolutional neural network with that of the full-precision convolutional neural network.

MNIST [45] is a data set of handwritten digits, which contains a training set of 60,000 examples and a test set of 10,000 examples. And, all examples in the training and test data sets are binary images.

LeNet [46] is a convolutional neural network with three convolutional layers, two subsampling layers, two full connection layers, an input layer, and an output layer.

For implementation, we used the BMXnet [47], which provided basic binarization operations for convolutional neural networks. After experimentation, we got the results as shown in Table 3.


AccuracyModel size

Binary LeNet0.970.2 MB
Full-precision LeNet0.994.6 MB

From Table 3, it can be seen that(1)The accuracy of the binary LeNet is slightly lower than that of the full-precision LeNet. The accuracy reduced by using the binary LeNet is around .(2)The model size of the binary LeNet is much lower than that of the full-precision LeNet. The memory saved by binary LeNet is around .

In other words, by using the binary convolutional neural network instead of the traditional full-precision convolutional neural network, the accuracy is only slightly reduced, but the memory is largely saved. Therefore, the binary convolutional neural network is quite suitable for the mobile crowdsourcing network, where mobile devices are with limited storage resources. The above evaluation shows that NLPAS fulfills the fifth requirement listed in Section 1 (i.e., the accuracy requirement).

5.2. Computation Costs

The computation cost of NLPAS includes the time cost consumed by the binary LeNet model and those consumed by mathematical operations. To test these time costs, we conducted our experiment on a laptop with an Intel i7-4770hq processor and an ubuntu-18.04 operating system. Then, we used OPENSSL [48] as the cryptographic library.

For the binary LeNet, we take the features extracted by the last full-connection layer as the input vectors (i.e., and ). Therefore, the vector length is [46]. To provide a basic security level, we set , and the length of is . To make sure and , we set the lengths of and to be 500 bit. Then, we set the lengths of and to be 683 bit, so that the lengths of and are around 1024 bit.

After the initial settings, we can count the mathematical operations in the hiding and extracting phases as listed in Table 4. From Table 4, it can be seen that all mathematical operations are run over 1024 bit and 512 bit fields.



841660
0840
002
002
002
002
002

Then, we tested the time costs consumed by these mathematical operations on the above laptop, and the average results of running them for 1,000,000 times are shown in Table 5. From Table 5, it can be seen that the time costs of mathematical operations are at the level.


0.22

1.69
2.19
0.98
1.03
0.16
0.14

Taking the results in Table 5 into Table 4, we can get the computation costs of algorithms in NLPAS, as shown in Table 6. From Table 6, it can be seen that the computation cost of mathematical operations on the MU (i.e., time costs of and ) is much lower than that on the CS (i.e., ). Therefore, NLPAS is suitable for mobile crowdsourcing networks, where MU is with limited computation resources.



18.48178.489.00

The time costs of the binary LeNet and the full-precision LeNet are shown in Table 7, where the results are average values of running the feature extracting process for 1,000,000 times. From Table 7, it can be seen that the computation cost of feature extracting in NLPAS can be largely reduced by using the binary convolutional neural network instead of the full-precision convolutional neural network.


Binary LeNetFull-precision LeNet

56.61435.2

The above evaluation shows that NLPAS fulfills the fourth requirement listed in Section 1 (i.e., the computation cost requirement).

5.3. Implementation of NLPAS

To make sure that NLPAS can work well, we implemented it. In our experimental environment, there were one laptop and one computer. The laptop acts as the MU, and the computer acts as the CS. The result shows that the total running time in the auditing protocol is approximately 0.3 ms. Therefore, NLPAS is feasible for being deployed in the real world.

6. Conclusions

In this paper, we have proposed a noninteractive lightweight privacy-preserving auditing protocol on images in mobile crowdsourcing networks called NLPAS. NLPAS allows the mobile user to audit images stored on the crowdsourcing server without downloading them. Moreover, to achieve high efficiency, this paper introduced the binary convolutional neural network technique to the newly proposed auditing protocol and designed a novel privacy-preserving Hamming distance computation algorithm using basic mathematical operations. Experimental results show that NLPAS is feasible for real-world applications.

In this paper, we mainly focused on the privacy-preserving issue of the newly designed auditing protocol for mobile crowdsourcing networks. However, several more issues are to be addressed in the future. First, NLPAS does not consider the integrity of transmitted messages. Therefore, a new security protocol is needed to prevent these messages from being tampered by adversaries. Second, NLPAS used the binary convolutional neural network for extracting a binary vector from images. However, in many scenarios, feature vectors may be extracted using full-precision neural networks, which are not binarized. Therefore, a new technique is needed to convert the full-precision feature vector to a binarized one. To address these issues, future works are needed.

Data Availability

The data used to support the findings of this study are available at http://yann.lecun.com/exdb/mnist/.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This paper was supported by the NSFC (nos. 71402070 and 61101088), the NSF of Jiangsu Province (no. BK20161099), and the Jiangsu Provincial Key Laboratory of Computer Network Technology.

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Copyright © 2020 Juan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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