An anti-tampering model of sensitive data in link network based on blockchain technology

Abstract

In order to overcome the problems of traditional link network sensitive data anti tampering operation, such as long time-consuming and low data security, a tamper proof model of link network sensitive data based on blockchain technology is proposed. Calculate the evenly distributed random variables of sensitive node data and the difference of running distance to obtain the probability of meeting the sensitive data with other neighbor nodes, and determine the sensitive data in the link network; obtain the frequency domain of the sensitive data of the infected link network through the square difference function, and calculate the membership mean value of the infected data samples in the sensitive data; analyze the working principle of blockchain technology, Set the master key and public key of sensitive data, generate the encryption key of sensitive data of link network, and use blockchain technology to complete the design of tamper proof model of sensitive data in link network. The experimental results show that the shortest time-consuming of the proposed method is about 1 s, and the maximum tamper proof security factor is about 9.7.

Keywords

Blockchain technology link network sensitive data encounter probability square difference function membership mean

1. Introduction

The characteristics of link network sensitive data are similar to big data, with complex structure, diversified forms and time-varying characteristics [10]. Sensitive information can take sensitive image, sound, video and text as carriers in the link [6]. With the development of network technology, people take the network as the main way to transmit information. Because of the loopholes in the network, the probability of information being attacked in the transmission process is large [17]. If the attacked sensitive data is tampered with by lawbreakers, it will cause irreparable losses to enterprises and individuals. Therefore, it is very important to study the anti-tampering methods of sensitive data in link network and researchers in this field have done a lot of research on anti-tampering methods of sensitive data in link networks.

In reference [7], an anti-tampering method for network sensitive data based on dynamic key is proposed. This method uses non retransmission packets to update the key of both sides in the communication process, encrypts and decrypts the sensitive data through XOR algorithm. The message and key are used as the input of Hash algorithm to authenticate the integrity of the data, so as to realize the anti-tampering of the sensitive data in the link network. The method does not build the link network model, and the data encryption takes a long time, and there is low efficiency of execution. In reference [14], an anti-tampering method for sensitive data based on bilinear mapping is proposed. This method combines attribute proxy’s re encryption mechanism and bilinear mapping, designs key transfer algorithm and redundant query tag generation algorithm to complete the encryption and decryption of sensitive data. This method does not analyze the transmission characteristics of sensitive data in the network link. It takes a long time to decrypt sensitive data and has low execution efficiency. Reference [9] proposes an anti-tampering method for sensitive data based on the characteristics of social network. This method uses the number of users’ concerns, ID creation time and ID as the parameters and initial values of the encryption function, and obtains the key sequence through the Tent mapping and Logistic mapping to realize the anti-tampering of sensitive data. This method does not encrypt and decrypt data on the basis of the link network model, resulting in the low data security factor, and there is a problem of poor data security.

In order to solve the shortcomings of the above methods, this paper proposes to design an anti-tampering model of sensitive data in link network based on blockchain technology. By analyzing the process of the sensitive node data meeting with the neighbor data in the link network, the evenly distributed random variables of the sensitive node data and the difference of the running distance are calculated to obtain the probability of meeting the sensitive data with other neighbor nodes, and determine the sensitive data in the link network. The frequency domain of the infected sensitive data in the link network is obtained by the square difference function, and the sensitivity is calculated. The membership mean value of infected data samples in the data is used to complete the screening of sensitive data; the working principle of blockchain technology is analyzed, the key for encrypting sensitive data is set, and the encryption key of sensitive data of link network is generated, to complete the design of anti-tampering model of sensitive data in link network by using blockchain technology. The specific steps of this paper are as follows:

This paper analyzes the process of data encounter between sensitive node and neighbor node in link network, and calculates the uniform distribution random variable of sensitive node data and the difference of running distance, to obtain the probability of meeting sensitive data with other neighbor node data, and determine the sensitive data in link network;

The frequency domain of the infected sensitive data in the link network is obtained by the square difference function, and the membership mean value of the infected data samples in the sensitive data is calculated to complete the screening of the sensitive data;

This paper analyzes the working principle of blockchain technology, and sets the key of encrypting sensitive data, to generate the encryption key of sensitive data in link network. Blockchain technology is used to complete the design of anti-tampering model of sensitive data in link network.

2. Acquisition and screening of sensitive data of link network based on blockchain technology

2.1. Acquisition of sensitive data in link network based on encounter probability

In order to effectively prevent tampering of sensitive data in the link network, the probability of the sensitive node data meeting with other neighbor nodes in the link network is analyzed. In this paper, the method of calculating the encounter probability is used to obtain the sensitive data in the link network. In the link network, by calculating the encounter probability, we can determine the frequency of the sensitive node data in the link network, and provide the basis for its accurate acquisition. The movement of sensitive node data and the running process of node neighbor’s discovery mechanism are independent of each other [15, 20]. In order to obtain the sensitive node data in the link network, the movement characteristics of the sensitive node data in the link network and the encounter probability of its neighbor node data are excluded.

When a node i meets other nodes in the network, at any time $T_{s}$ , the node will send neighbor detection information; assuming that the variable $X_{i}$ is a uniformly distributed random variable in the interval $[0, T_{s}]$ , $P (X_{i} ⩽ a)$ stands for the distribution function of probability of $X_{i}$ , namely $\begin{matrix} (1) & P (X_{i} ⩽ a) = \{\begin{matrix} 0 & a < 0 \\ \frac{a}{T_{s}} & 0 ⩽ a ⩽ T_{s} \\ 1 & a > T_{s} \end{matrix} \end{matrix}$

When the sensitive node i and node j meet in the network, $X = min (X_{i}, X_{j})$ , and $X_{i}$ and $X_{j}$ are independent each other. In this case, the relationship between sensitive nodes in the link network is as follows: $\begin{matrix} (2) & P (X ⩽ a) = P (min (X_{i}, X_{j}) ⩽ a) = 1 - P (X_{i} > a) \times P (X_{j} > a) \end{matrix}$

The relationship between neighbor discovering scan cycle and link duration is shown in Fig. 1.

Fig. 1.

Relationship between neighbor discovering scan cycle and link duration.

On this basis, let $T_{x}$ represent the distance difference between the sensitive node and the neighbor node in the link network as: $\begin{matrix} (3) & T_{x} = T - X \end{matrix}$

Where T represents the ideal distance of the sensitive node data operation in the link network, and X represents the actual distance of the sensitive node data operation in the link network.

After obtaining the data running distance of two kinds of sensitive nodes, the probability of meeting them is analyzed as: $\begin{matrix} (4) & P (T_{x} ⩽ x) = P (X ⩾ T - x) = \int_{0}^{+ \infty} P (X ⩾ b - x ∣ T = b) \cdot f_{T} (b) \cdot d b = \int_{0}^{+ \infty} [1 - P (X < b - x)] \cdot f_{T} (b) \cdot d b \end{matrix}$

In the formula, when $x ⩽ 0$ , it indicates $T < X$ , that is, the duration of data encounter between the two nodes is less than the time when any node sends out the first neighbor detection information. At this time, the occurrence of data encounter of sensitive nodes in the link network cannot be perceived [4, 8, 12]. In this case, the probability of the encounter between the motion characteristics of the sensitive node data and the data of its neighbors in the link network needs to be obtained according to the encounter duration of the node.

Let $F_{T_{f}} (t)$ describe the probability distribution function of the duration after the sensitive node data meets the neighbor node as: $\begin{matrix} (5) & F_{T_{f}} (t) = P (T_{f} ⩽ t) = \frac{P (T_{x} ⩽ t) - P (T_{x} ⩽ 0)}{1 - P (T_{x} ⩽ 0)} \end{matrix}$

Where $T_{f}$ represents the duration between nodes in the link, and P represents the duration coefficient after the data of sensitive nodes meet.

By analyzing the data of sensitive nodes and their neighbors in the same running area in the link network, the uniform distribution of the data of sensitive nodes and the distance difference between them are calculated to obtain the probability of meeting sensitive data with other neighbor nodes, so as to determine the sensitive data in the link network.

2.2. Screening of sensitive data in link network based on square difference function

On the basis of the above sensitive data acquisition, the sensitive data of the link network is filtered by the square difference function to provide more accurate data for subsequent encryption. In this paper, the sensitive data is updated and smoothed in the genetic iteration state, and the sensitive data of the link network is screened by the square difference function.

The sensitive data of link network is easy to be infected by other data viruses in the running area. It is necessary to analyze the infection probability of sensitive data in operation.

Let $k (W_{u}, {\overline{W}}_{v})$ represent the probability of successful transmission of information from infectious node u to susceptible node v as follows: $\begin{matrix} (6) & k (W_{u}, {\overline{W}}_{v}) = W_{u} {\overline{W}}_{v} \end{matrix}$

Where ${\overline{W}}_{v}$ describes the probability of v receiving information in the network; $W_{u}$ describes the probability of node u meeting any node in the network; k describes the probability function.

If the sensitive data in the link network is infected by the data of other neighbor nodes, the time required for the sensitive data to be infected in the link network is analyzed.

$E M_{rd}$ represents the average meeting time of two nodes’ data in the same area, and N describes the total number of nodes in the network; $τ_{u}$ is the time spent by node u to other nodes, then the time of mutual infection is as follows: $\begin{matrix} (7) & τ_{u} = \frac{E M_{rd}}{N - 1} \end{matrix}$

Let $λ_{u}$ represent the contact rate between the sensitive node and any node in the network. $\begin{matrix} (8) & λ_{u} = \frac{N - 1}{E M_{rd}} \end{matrix}$

Where N represents the total number of contact points, $E M_{rd}$ represents the average meeting time of two node data in the same region.

In general, the contact time between nodes satisfies the exponential distribution [2, 18, 19], and the probability of node u meeting any node in the network is $W_{u}$ ,that is: $\begin{matrix} (9) & W_{u} = 1 - e^{- Λ_{u} λ_{u}} \end{matrix}$

Where $Λ_{u}$ describes the information storage time of node u in the network, and e represents the encounter probability error of any node.

On the basis of the above analysis, the frequency domain of the infected sensitive data of the link network is obtained by the square difference function $\begin{matrix} (10) & p (i) = s (i) + ξ (i) \otimes ϕ (i) = ξ h [s (i)] \partial_{σ} \end{matrix}$

Where $p (i)$ represents the instantaneous amplitude, $s (i)$ represents the frequency domain complex signal of sensitive data, $ϕ (i)$ represents the frequency harmonic size of sensitive data, $ξ h [s (i)]$ represents the maximum membership degree of sensitive data, and $\partial_{σ}$ represents the type of sensitive data infected.

At this time, the sensitive data can be filtered by obtaining the subordinate mean value of the infected data samples in the sensitive data $\begin{matrix} (11) & R_{i} = \frac{\sum h [s (i)]}{G_{i}} \end{matrix}$

Where $G_{i}$ represents the total number of infected sensitive data.

In the link network sensitive data screening, the probability of infected sensitive data is determined. The frequency domain of the infected link network sensitive data is obtained by the square difference function, and the membership mean value of the infected data samples in the sensitive data is obtained to complete the screening of sensitive data.

3. Anti-tampering of sensitive data in link network based on blockchain technology

3.1. Operation principle of blockchain technology

Blockchain technology mainly solves the security and mutual trust problems in the transaction process. The overall framework of blockchain usually includes application layer, data layer, contract layer, network layer, incentive layer and consensus layer [5, 16]. The operation of blockchain includes consensus mechanism, request submission, new block generation and transaction verification. The operation principle of blockchain is shown in Fig. 2.

Fig. 2.

Operation principle of blockchain.

In this paper, blockchain technology is applied to the anti-tampering of sensitive data in the link network. When the user sends the storage request in the storage phase, the transmission leader node T broadcasts in the cluster to verify the validity of the user’s signature. If it is valid, the encrypted data based on attributes will be temporarily stored in the log by the transmission leader node T, and then sent to the public nodes $T_{n}$ copy the relevant information. When half of the nodes have received the information in the cluster, they send a confirmation message to the user. After the user receives the message, it transmits the leader node T to the public nodes $T_{n}$ , informing that the data status has been submitted. The transport node transmits the storage information to the storage node, and the node M responsible for the storage transmits the broadcast storage request to $M_{i}$ in the cluster. After completing data storage in the network, most of the nodes will feed back the information to the requester, to complete the anti-tampering of the sensitive data of the link network in the blockchain platform [1, 13].

3.2. Design of anti-tampering model for sensitive data of link network based on blockchain technology

According to the working principle of anti-tampering of sensitive data in link network of blockchain technology, and based on the screened sensitive data, the anti-tampering model of sensitive data in link network is constructed.

Firstly, the sensitive data of link network is initialized. $\begin{matrix} (12) & Setup (λ, U) \to (GP, MSK, PK) \end{matrix}$

Where U describes the attribute set of sensitive data and λ describes the security parameters of sensitive data.

The sensitive data of the initial link network is mapped. Let G represent the additive cyclic group with the order of p; g describes the generator of the additive cyclic group G, which conforms to the following bilinear mapping, that is: $\begin{matrix} (13) & e : G \times G \to G_{T} \end{matrix}$

After the sensitive data of link network is initialized, the key is set by blockchain technology. Suppose that $MSK$ represents the master key of sensitive data, and its expression is as follows $\begin{matrix} (14) & MSK = (g^{α}, a) \end{matrix}$

$PK$ represents the public key of sensitive data of link network, and its expression is as follows: $\begin{matrix} (15) & PK = (g, g_{1}, g^{a}, e {(g, g)}^{α}, H_{x}) \end{matrix}$

After setting the master key and public key, the encryption key of the sensitive data of the link network is generated. $\begin{matrix} (16) & KeyGen (GP, PK, MSK, S_{A}) \to (S K_{A}, P K_{A}) \end{matrix}$

Where $S_{A} \subseteq U$ represents the set of input attributes, the system master key is $MSK$ , the public parameter is $GP$ , and the random integer is $t \in Z_{p}$ . To generate user’s corresponding private key $S K_{A}$ and $P K_{A}$ [3, 21], the expressions are as follows: $\begin{array}{l} (17) & S K_{A} = \{\begin{matrix} S_{A} \\ K_{A} = g^{α} g^{at} \\ L_{A} = g^{t} \\ {[K_{X} = H_{3} {(x)}^{t}]}_{x \in S_{A}} \end{matrix}\} \\ (18) & P K_{A} = g^{S K_{A}} \end{array}$

Similarly, the private key $S K_{B}$ and $P K_{B}$ of the shared requester is obtained.

In order to ensure the security of the anti-tampering of the sensitive data in the link network, it is necessary to set the encryption key after the encryption of the above key, so as to prevent the sensitive data of the link network from being tampered with maliciously. In the encryption key setting of sensitive data, it is assumed that the master data of sensitive data information is $θ ϵ Z_{p}$ . Shared structure ( $M^{'}$ , $ρ^{'}$ ) is established on the basis of attribute set $S_{B}$ , where $M^{'}$ is $l^{'} \times n^{'}$ matrix. The main function of the function $ρ^{'}$ is to map the rows existing in $M^{'}$ as attributes and use the private key $P K_{B}$ , $S K_{A}$ and the common parameter $GP$ to calculate the encryption key $r k_{A \to B}$ [11, 22], namely: $\begin{array}{l} (19) & \{\begin{matrix} r k_{1} = K^{H_{5} (δ)} g_{1}^{θ} = g^{α} g^{at} g_{1}^{θ} \\ r k_{2} = g^{θ} \\ r k_{3} = L^{H_{5} (δ)} \\ r k_{4} = C_{(M^{'}, ρ^{'})}^{'} \\ R_{x} = K_{x}^{H_{5} (δ)} \end{matrix} \\ (20) & r k_{A \to B} = (S_{A}, r k_{1}, r k_{2}, r k_{3}, r k_{4}, R_{x}) \end{array}$

The integers $s, y_{2}, \dots, y_{n} \in Z_{p}$ are selected to construct column vector υ, and the expression of is as follows: $\begin{matrix} (21) & υ = (s, y_{1}, y_{2}, \dots, y_{n}) \end{matrix}$

Where, s describes the secret shared by the owner of sensitive data.

In this case, there is the following formula: $\begin{matrix} (22) & ε_{i} = υ M_{i} \end{matrix}$

Where, $M_{i}$ describes the i-th row vector existing in the matrix; $ε_{i}$ describes the effective part of the matrix.

After the key is set, the sensitive data of the link network is encrypted to prevent the sensitive data from being tampered with. Suppose that the sensitive data is m and the public key is not $P K_{A}$ in the link network. The sharing permission $(M, ρ)$ and reading permission are set in advance to calculate and store ciphertext information in the cloud server. Sharing permission $(M, ρ)$ , reading permission and system public key $PK$ and metadata data $Data \to {LC, w, IN}$ are input, where $IN$ describes the item number corresponding to the information; w describes the information keywords; $LC$ describes the storage location of the original information ciphertext in the cloud, and stores the output ciphertext $CT$ in the blockchain as follows: $\begin{matrix} (23) & \{\begin{matrix} A_{1} = Data \cdot e {(g, g)}^{α s}, A_{2} = g^{s}, A_{3} = g_{1}^{s} \\ B_{1} = {(g^{α})}^{ε_{1}} H_{1} {[ρ (1)]}^{- γ_{1}}, \dots, B_{l} = {(g^{α})}^{ε_{1}} H_{l} {[ρ (1)]}^{- γ_{l}} \\ C_{1} = g^{r_{1}}, \dots, C_{l} = g^{r_{l}} \end{matrix} \end{matrix}$

Let ϕ represent the important component in ciphertext, which can be encrypted by ciphertext $CT$ and encryption key $r k_{A \to B}$ . The anti-tampering model of sensitive data in link network is constructed as follows: $\begin{matrix} (24) & ϕ = \frac{\frac{e (A_{2}^{'}, r k_{1})}{e (A_{3}^{'}, r k_{2})}}{\prod_{i \in I} {[e (B_{i}^{'}, r k_{3}) e (C_{i}^{'}, R_{ρ (1)}^{'})]}^{ω_{i}}} \end{matrix}$

Where φ represents the important component of sensitive data ciphertext in the link network, e represents the key node of sensitive data, $A_{2}^{'}$ and $A_{3}^{'}$ represent the local encryption density value of sensitive data, and r represents the encryption deviation value of sensitive data.

The anti-tampering process of sensitive data of link network based on blockchain technology is shown in Fig. 3.

Fig. 3.

Flow chart of anti-tampering of sensitive data in link network based on blockchain technology.

In the construction of the anti-tampering model of the sensitive data in the link network based on the blockchain technology, the sensitive data to be encrypted is initialized, and then the key is set, including the master key and public key settings, to generate the encryption key of the sensitive data in the link network. In order to ensure the security of the sensitive data of the link network, the key is set again after the above key is set. The encryption key is set to prevent the sensitive data in link network from being tampered maliciously. On this basis, the block chain technology is used to design the anti-tampering model of the sensitive data in link network.

4. Experimental analysis

4.1. Experimental scheme

In order to verify the overall effectiveness of the proposed method, it needs to test the proposed method, which is completed in the Intel Core i5-3337U environment. The experimental operating system is Windows 8, CPU is 2.40 GHz, and the experimental data are recorded and analyzed by SPSS 10.0.

The experimental parameters are shown in Table 1.

Table 1
Experimental parameters

Parameter Value

Sample data / GB 2

Sampling interval / s 0.2

Noise interference / dB −2–5

Iterations / times 20

Parameter	Value
Sample data / GB	2
Sampling interval / s	0.2
Noise interference / dB	−2–5
Iterations / times	20

On the basis of the above experimental environment and parameter design, the anti-tampering model of sensitive data in link network based on blockchain technology, the anti-tampering method of network sensitive data based on dynamic key, the anti-tampering method of sensitive data based on bilinear mapping, and the anti-tampering method of sensitive data based on social network characteristics are tested respectively to extract the time-consuming of sensitive data of link network and the security of anti-tampering as the experimental index.

4.2. Analysis of experimental results

4.2.1. Time-consuming analysis of sensitive data extraction in link network

The time-consuming of sensitive data extraction in link network can reflect the performance of the method. The experiment compares the time-consuming of the proposed method, the anti-tampering method for network sensitive data based on dynamic key, the anti-tampering method for sensitive data based on bilinear mapping, and the anti-tampering method for sensitive data based on social network characteristics. The experimental results are shown in Fig. 4.

Fig. 4.

Time-consuming comparison of sensitive data extraction in link network.

Analyzing the data in Fig. 4, we can see that with the change of extraction times, the time consumption of the four methods for the extraction of sensitive data in the link network is different. Among them, the extraction time of the proposed method is about 1 s, while that of the other three methods is about 2.4 s, 2.3 s and 1.9 s, respectively. In contrast, the time-consuming of the proposed method is shorter, because the proposed method analyzes the encounter process of sensitive node data and neighbor node data, and calculates the evenly distributed random variables of sensitive node data and the difference of running distance, so as to obtain the encounter probability of sensitive data with other neighbor node data, and determine the sensitive data in the link network. The frequency domain of the infected sensitive data in the link network is obtained by the square difference function, and the membership mean value of the infected data samples in the sensitive data is calculated to complete the screening of the sensitive data, which avoids the extraction of other data and reduces the extraction time.

4.2.2. Security analysis on anti-tampering with sensitive data in link network

In the anti-tampering security experiment of sensitive data in the link network, the safety factor ϑ is set and the safety factor is taken in the interval $[0, 10]$ . The higher the safety factor is, the better the anti-tampering effect of the data is. The experimental results are shown in Fig. 5.

Fig. 5.

Comparison of anti-tampering safety factors of different methods.

According to the data in Fig. 5, under the same experimental environment, the security factor of the proposed method for anti-tampering with the sensitive data of the link network is higher, and the maximum is about 9.7, while that of the other traditional methods for anti-tampering with the sensitive data of the link network is about 5.1 and 5.2 respectively. This is because the proposed method sets the key for encrypting sensitive data, generates the encryption key for sensitive data of link network, and uses block chain technology to complete the anti-tampering model of sensitive data of link network, thus improving its security performance.

5. Conclusions

At present, there are problems of low execution efficiency and poor security in anti-tampering methods for link network sensitive data. This paper proposes the design of anti-tampering model of sensitive data in link network based on blockchain technology. It analyzes the process of the encounter between sensitive node data and neighbor node data, calculates the uniform distribution random variables of sensitive node data and the difference of running distance, so as to obtain the sensitive data. The data encounter probability of other neighbor nodes is used to determine the sensitive data in the link network; the frequency domain of the infected sensitive data in the link network is obtained through the square difference function, and the membership mean value of the infected data sample in the sensitive data is calculated to complete the screening of the sensitive data; the working principle of the blockchain technology is analyzed, and the key for encrypting the sensitive data is set to generate the encryption of the sensitive data in the link network. Block chain technology is used to complete the design of anti-tampering model for sensitive data of link network. Compared with traditional methods, the proposed method has the following advantages:

The shortest time-consuming of the proposed method to extract sensitive data of link network is about 1 s, which improves the work efficiency.

The maximum security factor of the proposed method to prevent tampering with sensitive data of link network is about 9.7, which verifies the reliability of the proposed method.

Footnotes

Acknowledgements

This work was supported by Jiangsu Government Scholarship for Overseas Studies under grant no. JS-2019-120.

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An anti-tampering model of sensitive data in link network based on blockchain technology

Abstract

Keywords

1. Introduction

2. Acquisition and screening of sensitive data of link network based on blockchain technology

2.1. Acquisition of sensitive data in link network based on encounter probability

3. Anti-tampering of sensitive data in link network based on blockchain technology

3.1. Operation principle of blockchain technology

4.1. Experimental scheme

Table 1 Experimental parameters Parameter Value Sample data / GB 2 Sampling interval / s 0.2 Noise interference / dB −2–5 Iterations / times 20

4.2.1. Time-consuming analysis of sensitive data extraction in link network

Footnotes

Acknowledgements

References

Table 1
Experimental parameters

Parameter Value

Sample data / GB 2

Sampling interval / s 0.2

Noise interference / dB −2–5

Iterations / times 20