# Legal Protection of Artificial Intelligence Data and Algorithms from the Perspective of Internet of Things Resource Sharing

Authors: Na An; Xiaolei Wang
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

There are few laws and regulations related to privacy protection in the existing artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects my country’s current weak protection of big data privacy. In order to solve the problem of sharing artificial intelligence data and algorithms, it becomes very important to study the legal protection of artificial intelligence data and algorithms from the perspective of Internet of Things resource sharing. This article is aimed at studying the use of laws to protect artificial intelligence data and algorithms. Aiming at reducing the bullwhip effect, a most effective bullwhip effect model derivation algorithm is proposed. This method can not only share customer demand information with members at all levels in the supply chain but also achieve information sharing among members at all levels. Calculate the proportion of the overall time of the program through multiple statistical data (m=30, k=12; and m=60, k=15), and extract two special values representing the overall situation (m=30, k=12; m=60, k=15). Most of the time consumption of this program is concentrated in the secret distribution stage, accounting for about 80% on average.


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## Body

## 1. Introduction

### 1.1. Background

With the rapid development of mobile internet technology, the Internet of Things, and other technologies, the era of intelligence has come, and human society has once again entered a new, large-scale production sharing and application data space represented by artificial intelligence, and an artificial intelligence data sharing environment. In this environment, our behavior, location, and all aspects of life are being digitally recorded and analyzed. The computer software and smart phone apps that are widely used nowadays realize remote control of work and family life through internet services, collect, sort, and analyze user interests and preference information, and transmit it to the associated electronic devices, which greatly changes the human experience. However, the current artificial intelligence data sharing environment in my country is still in a new state. Data information has not been processed, regulated, and uploaded to the artificial intelligence data sharing environment openly and transparently. As a result, our personal data information is approaching publicity and our lives have become transparent. Undeniably, citizen privacy is seriously threatened in the artificial intelligence data sharing environment [1]. There are few existing laws and regulations related to privacy protection in the artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects the current weak protection of big data privacy in my country. If the privacy protection problem of the artificial intelligence data sharing environment is not effectively solved and regulated, this will undoubtedly cause serious threats and severe challenges to the information order and security.


### 1.2. Significance

The protection of data and privacy is a hot topic at the moment, and discussions about it have never stopped. Nowadays, in the artificial intelligence data sharing environment, along with the widespread application and vigorous development of artificial intelligence technology, citizens’ information and data privacy is becoming more and more digitized. Privacy infringement issues are becoming more frequent, traditional privacy conflicts are also intensifying, the connotation and semantic scope of privacy have also changed, and the need for privacy protection is becoming more urgent. Although my country has promulgated some relevant laws and regulations such as the Cyber Security Law, it has laid the foundation for rectifying the big data environment. However, the current privacy protection laws and regulations and protection models still cannot meet my country’s effective and standardized management of the artificial intelligence data sharing environment, and further improvements are urgently needed. Therefore, this article is based on the artificial intelligence data sharing environment, drawing on foreign excellent experience, starting from reality, through a multiangle analysis and discussion on legislative protection, administrative agency supervision, judicial protection, and industry self-discipline models, and proposes a perfect artificial intelligence data sharing suitable for my country’s national conditions. A series of countermeasures for privacy protection and supervision in the environment have promoted citizens’ own basic rights and interests to be further protected, with a view to optimizing the artificial intelligence data sharing environment, establishing a clear new order, and maintaining social harmony and stability.


### 1.3. Related Work

The emergence of the concept of the Internet of Things provides a good network infrastructure platform for the ultimate realization of the ubiquitous computing environment [2]. Therefore, the Internet of Things will be another technological and economic wave in the global information industry after computers, the Internet, and mobile communication networks, and it will bring great opportunities and challenges to the high-speed information life. Lee discussed the design of a legal and regulatory framework for using artificial intelligence (AI) to enhance financing channels (financial inclusion) in the financial services market. He believes that the development of artificial intelligence should continue to adhere to the regulatory goals of market safety, consumer protection, and market integrity. However, to ensure equality and fairness, financing channels should become a clear policy choice. In the first part, he discussed how artificial intelligence can lead to systemic risks and market manipulation on trading platforms. For example, by studying the use of trading algorithms in the capital market, he identified the regulatory goals and possible regulatory methods of the P2P platform. In the second part, he discussed the use of artificial intelligence to provide consumers with investment advice, such as financial advice provided by roboadvisors, how to narrow the gap between investment advisors and provide consumers with financing channels, but the method to narrow the gap has not been found yet [3]. Yildirim and Tatar pointed out that this research focuses on the interoperability and resource sharing issues in wireless sensor networks (WSN) operating under the Internet of Things (IoT) and cyber-physical systems (CPS). Taking into account the scale of the IoT/CPS project, due to hardware/software limitations and heterogeneity, traditional WSN virtualization technology is still incapable. To this end, in his research, he proposed a proxy-based server system method that improves resource sharing between heterogeneous WSNs in IoT/CPS providers. According to this method, a software agent framework is introduced. With the help of a framework called Firat Virtual WSN Framework (FVWSN), the client can move the command/query and data fusion/aggregation algorithms used on its local network to the provider side and run them remotely or automatically, but remotely. There may be a delay in control [4]. Li et al. pointed out that in a smart city, all kinds of user data are stored in electronic devices, making everything smart. Smart phones are the most widely used electronic devices and the hub of all smart systems. However, the current smart phones do not yet have the ability to manage the user’s sensitive data, and they are faced with the problem of privacy leakage caused by excessive data collection. Excessive data collection, that is, smart phone applications that collect user data within their authority beyond their original functions, is quickly becoming one of the most serious potential security hazards in smart cities. In this article, he examines the current state of data overcollection and studies some of the most common cases of data overcollection. They proposed a mobile cloud framework, which is a positive way to eliminate excessive data collection, but security risks still exist [5].


### 1.4. Main Content

Through a basic overview of artificial intelligence data and algorithms, the article compares the legal status of artificial intelligence from different angles, and deeply analyzes the legal status of artificial intelligence data. Measures are proposed to protect and regulate the legal status of artificial intelligence data and algorithms, respectively, and create innovations for the healthy and orderly development of artificial intelligence. Then, using quantitative analysis methods, the value of information sharing in the supply chain was quantitatively analyzed. Quantitative analysis method (quantitative analysis method) is a method of analyzing the quantitative characteristics, quantitative relationships, and quantitative changes of social phenomena. At the end of the thesis, an identity-based public encryption algorithm (IBE) is adopted, and a secret sharing strategy is introduced in IBE. This solution is mainly aimed at cloud computing service providers rather than trustworthy, so the corresponding threshold strategy is adopted to reduce the threat of data leakage from a single untrusted service provider, and private data such as identity information is authorized by the data provider.


## 1.1. Background

With the rapid development of mobile internet technology, the Internet of Things, and other technologies, the era of intelligence has come, and human society has once again entered a new, large-scale production sharing and application data space represented by artificial intelligence, and an artificial intelligence data sharing environment. In this environment, our behavior, location, and all aspects of life are being digitally recorded and analyzed. The computer software and smart phone apps that are widely used nowadays realize remote control of work and family life through internet services, collect, sort, and analyze user interests and preference information, and transmit it to the associated electronic devices, which greatly changes the human experience. However, the current artificial intelligence data sharing environment in my country is still in a new state. Data information has not been processed, regulated, and uploaded to the artificial intelligence data sharing environment openly and transparently. As a result, our personal data information is approaching publicity and our lives have become transparent. Undeniably, citizen privacy is seriously threatened in the artificial intelligence data sharing environment [1]. There are few existing laws and regulations related to privacy protection in the artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects the current weak protection of big data privacy in my country. If the privacy protection problem of the artificial intelligence data sharing environment is not effectively solved and regulated, this will undoubtedly cause serious threats and severe challenges to the information order and security.


## 1.2. Significance

The protection of data and privacy is a hot topic at the moment, and discussions about it have never stopped. Nowadays, in the artificial intelligence data sharing environment, along with the widespread application and vigorous development of artificial intelligence technology, citizens’ information and data privacy is becoming more and more digitized. Privacy infringement issues are becoming more frequent, traditional privacy conflicts are also intensifying, the connotation and semantic scope of privacy have also changed, and the need for privacy protection is becoming more urgent. Although my country has promulgated some relevant laws and regulations such as the Cyber Security Law, it has laid the foundation for rectifying the big data environment. However, the current privacy protection laws and regulations and protection models still cannot meet my country’s effective and standardized management of the artificial intelligence data sharing environment, and further improvements are urgently needed. Therefore, this article is based on the artificial intelligence data sharing environment, drawing on foreign excellent experience, starting from reality, through a multiangle analysis and discussion on legislative protection, administrative agency supervision, judicial protection, and industry self-discipline models, and proposes a perfect artificial intelligence data sharing suitable for my country’s national conditions. A series of countermeasures for privacy protection and supervision in the environment have promoted citizens’ own basic rights and interests to be further protected, with a view to optimizing the artificial intelligence data sharing environment, establishing a clear new order, and maintaining social harmony and stability.


## 1.3. Related Work

The emergence of the concept of the Internet of Things provides a good network infrastructure platform for the ultimate realization of the ubiquitous computing environment [2]. Therefore, the Internet of Things will be another technological and economic wave in the global information industry after computers, the Internet, and mobile communication networks, and it will bring great opportunities and challenges to the high-speed information life. Lee discussed the design of a legal and regulatory framework for using artificial intelligence (AI) to enhance financing channels (financial inclusion) in the financial services market. He believes that the development of artificial intelligence should continue to adhere to the regulatory goals of market safety, consumer protection, and market integrity. However, to ensure equality and fairness, financing channels should become a clear policy choice. In the first part, he discussed how artificial intelligence can lead to systemic risks and market manipulation on trading platforms. For example, by studying the use of trading algorithms in the capital market, he identified the regulatory goals and possible regulatory methods of the P2P platform. In the second part, he discussed the use of artificial intelligence to provide consumers with investment advice, such as financial advice provided by roboadvisors, how to narrow the gap between investment advisors and provide consumers with financing channels, but the method to narrow the gap has not been found yet [3]. Yildirim and Tatar pointed out that this research focuses on the interoperability and resource sharing issues in wireless sensor networks (WSN) operating under the Internet of Things (IoT) and cyber-physical systems (CPS). Taking into account the scale of the IoT/CPS project, due to hardware/software limitations and heterogeneity, traditional WSN virtualization technology is still incapable. To this end, in his research, he proposed a proxy-based server system method that improves resource sharing between heterogeneous WSNs in IoT/CPS providers. According to this method, a software agent framework is introduced. With the help of a framework called Firat Virtual WSN Framework (FVWSN), the client can move the command/query and data fusion/aggregation algorithms used on its local network to the provider side and run them remotely or automatically, but remotely. There may be a delay in control [4]. Li et al. pointed out that in a smart city, all kinds of user data are stored in electronic devices, making everything smart. Smart phones are the most widely used electronic devices and the hub of all smart systems. However, the current smart phones do not yet have the ability to manage the user’s sensitive data, and they are faced with the problem of privacy leakage caused by excessive data collection. Excessive data collection, that is, smart phone applications that collect user data within their authority beyond their original functions, is quickly becoming one of the most serious potential security hazards in smart cities. In this article, he examines the current state of data overcollection and studies some of the most common cases of data overcollection. They proposed a mobile cloud framework, which is a positive way to eliminate excessive data collection, but security risks still exist [5].


## 1.4. Main Content

Through a basic overview of artificial intelligence data and algorithms, the article compares the legal status of artificial intelligence from different angles, and deeply analyzes the legal status of artificial intelligence data. Measures are proposed to protect and regulate the legal status of artificial intelligence data and algorithms, respectively, and create innovations for the healthy and orderly development of artificial intelligence. Then, using quantitative analysis methods, the value of information sharing in the supply chain was quantitatively analyzed. Quantitative analysis method (quantitative analysis method) is a method of analyzing the quantitative characteristics, quantitative relationships, and quantitative changes of social phenomena. At the end of the thesis, an identity-based public encryption algorithm (IBE) is adopted, and a secret sharing strategy is introduced in IBE. This solution is mainly aimed at cloud computing service providers rather than trustworthy, so the corresponding threshold strategy is adopted to reduce the threat of data leakage from a single untrusted service provider, and private data such as identity information is authorized by the data provider.


## 2. Internet of Things Resource Sharing Protection Method

### 2.1. Data Sharing System Framework

Through the C/S structure model, the overall physical architecture of the data collection and sharing platform system is designed [6, 7]. The collection terminal connects the camera and the RFID antenna device through the network, processes the data through the collection terminal such as ETAG, and transmits the data to the collection service through the network, such as shown in Figure 1.Figure 1
Overall physical architecture diagram.And according to the ZMQ protocol, the picture data is transmitted to the picture processing subroutine [8], and the nonpicture data is transmitted to Rabbit MQ (message queue system) in the way of nonpicture data, and other data is stored in Oracle. Figure 2 shows the logical architecture diagram of the server.Figure 2
Server logical architecture diagram.The physical architecture of the client is to connect various data collection devices with the collection client to obtain the connection from the client to the collection server [9, 10]. The physical architecture of the client is shown in Figure 3.Figure 3
Client physical architecture diagram.


### 2.2. Value of Information Sharing

Since the existence of the bullwhip effect was confirmed, many scholars at home and abroad have tried to quantify the harm of the bullwhip effect to the supply chain through various methods.


#### 2.2.1. Modeling and Derivation of the Bullwhip Effect

Assume a simple supply chain [11], namely a manufacturer and a retailer. Manufacturers only sell products to retailers in this supply chain, assuming that the demand of the end customer is the Ft variable.
(1)Ft=μ+λFt−1+ωt.ωt refers to the variation error of the demand variable, and the error is independent in each period, ωt~N0,σ2; σ is assumed to be much smaller than μ, so as to ensure that the demand variable Ft>0. Obviously, when λ=0 in formula (1), the demand variable is Ft~Nμ,σ2.Whent⟶∞(2)EFt=μ1−λVarFt=σ21‐λ2.It can be seen from formula (2) that EFt fluctuates up and down μ/1−λ, but EFt and EFt−1 are not necessarily the same. Many scholars who study supply chain management information sharing have adopted the above method for demand variables.The order pointyt is set at any time, which contains the following functions:
(3)yt=LF¯t+ϕLStIn formula (3), L represents the order lead time and is a constant; Ft¯ represents the estimated value of demand expectation; St represents the estimated value of standard deviation; φ refers to the coefficient of supply service level.From the normal distribution function [12], when φ=1,3, the supply rate in L time is 84.1% and 99.8%. In the i stage, Di represents customer needs, which has the following forms:
(4)Ft¯=∑i=t−nt−1FinSt2=∑i=t−nt−1Fi−F¯t2n−1.In formula (4), n represents the observation period selected in the moving average method.Assume that the variableQt is the goods ordered by the retailer from the upstream manufacturer, and the quantity meets the following conditions:
(5)Qt=yt−yt−1+Ft−1.Substituting formula (3) and formula (4) into formula (5), respectively, after sorting out, we get
(6)Qt=1+LnFt−1−LnFt−n−1+ϕLSt−St−1.The order quantityQt in formula (6), taking its variance, sorts out
(7)VarQt=1+2Ln+2L2n21−λnVarFt+ϕ2LVarSt−St−1.This formula can be used as the quantitative formula BE of the bullwhip effect [13].
(8)BE=VarQtVarQt≥1+21−λnLL+nn2.When it is zero, the equal sign is taken in formula (8).The following forecast analysis adopts exponential smoothing method, suppose variableFt=αFt−1+1−αFt−1, where α represents the smoothing coefficient, and 0<α<1.Using exponential smoothing, the following quantitative formula BE can be derived:(9)BE=VarQtVarFt≥1+2Lα+2L2α22−α1−λ1−λβ.In formula (9), is the retailer’s smoothing coefficient, is the manufacturer’s smoothing coefficient, and β=1−α,0<β<1.


### 2.3. Information Sharing Value Analysis

#### 2.3.1. When there Is No Information Sharing

Assuming that the end customer demand variable isFt, and the adjacent periods do not affect each other, that is λ=0, the expression of Ft is
(10)Ft=μ+ωt.The parametersμ, ωt, and λ in Equation (10) have been explained in the modeling and derivation of the bullwhip effect, so I will not repeat them here [14, 15].Assuming that the inventory decision model adopted by the members at all levels in the supply chain iss,S, the order point of each member in the supply chain is
(11)ytk=Lk•utk¯.In formula (11), Lk represents the lead time for the kth level member in the supply chain to place an order with the k+1th level member and has the following formula:
(12)ut1¯=∑i=1nFt−1nutk¯=∑i=1n−1Qt−jk−1n,k≥2.In formula (12), u¯t1 represents the demand of first-level members, that is, customers in period t; u¯tk represents the demand of kth members in period t; n represents the number of observation periods of demand forecast Qt−jk−1; it represents the order quantity of the k−1th level member in tj period.Assuming thatQtk is used to represent the order quantity of the kth member in period t, combining Equations (5) and (7) can be derived to obtain the variance ratio between Qtk and the end customer demand Ft as
(13)VarQtkVarFt≥∏i=1k−11+2Lin+2Li2n2∀k.


#### 2.3.2. Information Sharing

Assuming that the supply chain realizes information sharing, based on the observed value of the demand, the average value of the next layer is predicted. The requirements are(14)ut¯=∑i=1nFt−1n.Order pointytk has
(15)ytk=Lk•utk¯+ϕLkStk.For the end customer demandFt, the supply chain variance is as follows:
(16)VarQtkVarFt≥1+2∑i=1k=1Lin+2∑i=1k=1Li2n2∀k.


#### 2.3.3. Comparative Analysis of No Information Sharing and Information Sharing

Through the discussion and analysis of the two situations of whether information sharing is implemented in the supply chain, it can be known that the impact of information sharing on the bullwhip effect is different [16]. Only according to the structure of (13) and (16) it can be seen that after information sharing in the supply chain, the bullwhip effect will be weakened. The following is a detailed analysis of the value of information sharing by combining (13) and (16).First, only the retailer closest to the final customer has demand information, and members of other levels at all levels can only predict demand information based on the order information of neighboring lower-level members. Because the demand on these orders has been modified and adjusted, resulting in a huge deviation from the actual customer demand, the ratio of its variance to the variance of customer demand is increasing exponentially [17]. Therefore, the lack of sharing of supply chain information will exacerbate the bullwhip effect.Second, after information sharing, members at every level of the supply chain can obtain the actual demand information of the end customer. Customer demand is not large, and the gap between the number of orders and customer demand will gradually widen. Therefore, the implementation of information sharing in the supply chain can effectively weaken the bullwhip effect.Use the two formulas (13) and (16) to select values for the three parameters of L, n, and k, respectively, calculate the data, then draw based on these data, and compare and explain in a more vivid way.LetLi=1, i=1,⋯,k, take k=1,4,and6, respectively, and plot according to the obtained data. Figure 4 shows the bullwhip effect of nonsharing and sharing of supply chain information [18].Figure 4
Comparison of the bullwhip effect without information sharing.


## 2.1. Data Sharing System Framework

Through the C/S structure model, the overall physical architecture of the data collection and sharing platform system is designed [6, 7]. The collection terminal connects the camera and the RFID antenna device through the network, processes the data through the collection terminal such as ETAG, and transmits the data to the collection service through the network, such as shown in Figure 1.Figure 1
Overall physical architecture diagram.And according to the ZMQ protocol, the picture data is transmitted to the picture processing subroutine [8], and the nonpicture data is transmitted to Rabbit MQ (message queue system) in the way of nonpicture data, and other data is stored in Oracle. Figure 2 shows the logical architecture diagram of the server.Figure 2
Server logical architecture diagram.The physical architecture of the client is to connect various data collection devices with the collection client to obtain the connection from the client to the collection server [9, 10]. The physical architecture of the client is shown in Figure 3.Figure 3
Client physical architecture diagram.


## 2.2. Value of Information Sharing

Since the existence of the bullwhip effect was confirmed, many scholars at home and abroad have tried to quantify the harm of the bullwhip effect to the supply chain through various methods.


### 2.2.1. Modeling and Derivation of the Bullwhip Effect

Assume a simple supply chain [11], namely a manufacturer and a retailer. Manufacturers only sell products to retailers in this supply chain, assuming that the demand of the end customer is the Ft variable.
(1)Ft=μ+λFt−1+ωt.ωt refers to the variation error of the demand variable, and the error is independent in each period, ωt~N0,σ2; σ is assumed to be much smaller than μ, so as to ensure that the demand variable Ft>0. Obviously, when λ=0 in formula (1), the demand variable is Ft~Nμ,σ2.Whent⟶∞(2)EFt=μ1−λVarFt=σ21‐λ2.It can be seen from formula (2) that EFt fluctuates up and down μ/1−λ, but EFt and EFt−1 are not necessarily the same. Many scholars who study supply chain management information sharing have adopted the above method for demand variables.The order pointyt is set at any time, which contains the following functions:
(3)yt=LF¯t+ϕLStIn formula (3), L represents the order lead time and is a constant; Ft¯ represents the estimated value of demand expectation; St represents the estimated value of standard deviation; φ refers to the coefficient of supply service level.From the normal distribution function [12], when φ=1,3, the supply rate in L time is 84.1% and 99.8%. In the i stage, Di represents customer needs, which has the following forms:
(4)Ft¯=∑i=t−nt−1FinSt2=∑i=t−nt−1Fi−F¯t2n−1.In formula (4), n represents the observation period selected in the moving average method.Assume that the variableQt is the goods ordered by the retailer from the upstream manufacturer, and the quantity meets the following conditions:
(5)Qt=yt−yt−1+Ft−1.Substituting formula (3) and formula (4) into formula (5), respectively, after sorting out, we get
(6)Qt=1+LnFt−1−LnFt−n−1+ϕLSt−St−1.The order quantityQt in formula (6), taking its variance, sorts out
(7)VarQt=1+2Ln+2L2n21−λnVarFt+ϕ2LVarSt−St−1.This formula can be used as the quantitative formula BE of the bullwhip effect [13].
(8)BE=VarQtVarQt≥1+21−λnLL+nn2.When it is zero, the equal sign is taken in formula (8).The following forecast analysis adopts exponential smoothing method, suppose variableFt=αFt−1+1−αFt−1, where α represents the smoothing coefficient, and 0<α<1.Using exponential smoothing, the following quantitative formula BE can be derived:(9)BE=VarQtVarFt≥1+2Lα+2L2α22−α1−λ1−λβ.In formula (9), is the retailer’s smoothing coefficient, is the manufacturer’s smoothing coefficient, and β=1−α,0<β<1.


## 2.2.1. Modeling and Derivation of the Bullwhip Effect

Assume a simple supply chain [11], namely a manufacturer and a retailer. Manufacturers only sell products to retailers in this supply chain, assuming that the demand of the end customer is the Ft variable.
(1)Ft=μ+λFt−1+ωt.ωt refers to the variation error of the demand variable, and the error is independent in each period, ωt~N0,σ2; σ is assumed to be much smaller than μ, so as to ensure that the demand variable Ft>0. Obviously, when λ=0 in formula (1), the demand variable is Ft~Nμ,σ2.Whent⟶∞(2)EFt=μ1−λVarFt=σ21‐λ2.It can be seen from formula (2) that EFt fluctuates up and down μ/1−λ, but EFt and EFt−1 are not necessarily the same. Many scholars who study supply chain management information sharing have adopted the above method for demand variables.The order pointyt is set at any time, which contains the following functions:
(3)yt=LF¯t+ϕLStIn formula (3), L represents the order lead time and is a constant; Ft¯ represents the estimated value of demand expectation; St represents the estimated value of standard deviation; φ refers to the coefficient of supply service level.From the normal distribution function [12], when φ=1,3, the supply rate in L time is 84.1% and 99.8%. In the i stage, Di represents customer needs, which has the following forms:
(4)Ft¯=∑i=t−nt−1FinSt2=∑i=t−nt−1Fi−F¯t2n−1.In formula (4), n represents the observation period selected in the moving average method.Assume that the variableQt is the goods ordered by the retailer from the upstream manufacturer, and the quantity meets the following conditions:
(5)Qt=yt−yt−1+Ft−1.Substituting formula (3) and formula (4) into formula (5), respectively, after sorting out, we get
(6)Qt=1+LnFt−1−LnFt−n−1+ϕLSt−St−1.The order quantityQt in formula (6), taking its variance, sorts out
(7)VarQt=1+2Ln+2L2n21−λnVarFt+ϕ2LVarSt−St−1.This formula can be used as the quantitative formula BE of the bullwhip effect [13].
(8)BE=VarQtVarQt≥1+21−λnLL+nn2.When it is zero, the equal sign is taken in formula (8).The following forecast analysis adopts exponential smoothing method, suppose variableFt=αFt−1+1−αFt−1, where α represents the smoothing coefficient, and 0<α<1.Using exponential smoothing, the following quantitative formula BE can be derived:(9)BE=VarQtVarFt≥1+2Lα+2L2α22−α1−λ1−λβ.In formula (9), is the retailer’s smoothing coefficient, is the manufacturer’s smoothing coefficient, and β=1−α,0<β<1.


## 2.3. Information Sharing Value Analysis

### 2.3.1. When there Is No Information Sharing

Assuming that the end customer demand variable isFt, and the adjacent periods do not affect each other, that is λ=0, the expression of Ft is
(10)Ft=μ+ωt.The parametersμ, ωt, and λ in Equation (10) have been explained in the modeling and derivation of the bullwhip effect, so I will not repeat them here [14, 15].Assuming that the inventory decision model adopted by the members at all levels in the supply chain iss,S, the order point of each member in the supply chain is
(11)ytk=Lk•utk¯.In formula (11), Lk represents the lead time for the kth level member in the supply chain to place an order with the k+1th level member and has the following formula:
(12)ut1¯=∑i=1nFt−1nutk¯=∑i=1n−1Qt−jk−1n,k≥2.In formula (12), u¯t1 represents the demand of first-level members, that is, customers in period t; u¯tk represents the demand of kth members in period t; n represents the number of observation periods of demand forecast Qt−jk−1; it represents the order quantity of the k−1th level member in tj period.Assuming thatQtk is used to represent the order quantity of the kth member in period t, combining Equations (5) and (7) can be derived to obtain the variance ratio between Qtk and the end customer demand Ft as
(13)VarQtkVarFt≥∏i=1k−11+2Lin+2Li2n2∀k.


### 2.3.2. Information Sharing

Assuming that the supply chain realizes information sharing, based on the observed value of the demand, the average value of the next layer is predicted. The requirements are(14)ut¯=∑i=1nFt−1n.Order pointytk has
(15)ytk=Lk•utk¯+ϕLkStk.For the end customer demandFt, the supply chain variance is as follows:
(16)VarQtkVarFt≥1+2∑i=1k=1Lin+2∑i=1k=1Li2n2∀k.


### 2.3.3. Comparative Analysis of No Information Sharing and Information Sharing

Through the discussion and analysis of the two situations of whether information sharing is implemented in the supply chain, it can be known that the impact of information sharing on the bullwhip effect is different [16]. Only according to the structure of (13) and (16) it can be seen that after information sharing in the supply chain, the bullwhip effect will be weakened. The following is a detailed analysis of the value of information sharing by combining (13) and (16).First, only the retailer closest to the final customer has demand information, and members of other levels at all levels can only predict demand information based on the order information of neighboring lower-level members. Because the demand on these orders has been modified and adjusted, resulting in a huge deviation from the actual customer demand, the ratio of its variance to the variance of customer demand is increasing exponentially [17]. Therefore, the lack of sharing of supply chain information will exacerbate the bullwhip effect.Second, after information sharing, members at every level of the supply chain can obtain the actual demand information of the end customer. Customer demand is not large, and the gap between the number of orders and customer demand will gradually widen. Therefore, the implementation of information sharing in the supply chain can effectively weaken the bullwhip effect.Use the two formulas (13) and (16) to select values for the three parameters of L, n, and k, respectively, calculate the data, then draw based on these data, and compare and explain in a more vivid way.LetLi=1, i=1,⋯,k, take k=1,4,and6, respectively, and plot according to the obtained data. Figure 4 shows the bullwhip effect of nonsharing and sharing of supply chain information [18].Figure 4
Comparison of the bullwhip effect without information sharing.


## 2.3.1. When there Is No Information Sharing

Assuming that the end customer demand variable isFt, and the adjacent periods do not affect each other, that is λ=0, the expression of Ft is
(10)Ft=μ+ωt.The parametersμ, ωt, and λ in Equation (10) have been explained in the modeling and derivation of the bullwhip effect, so I will not repeat them here [14, 15].Assuming that the inventory decision model adopted by the members at all levels in the supply chain iss,S, the order point of each member in the supply chain is
(11)ytk=Lk•utk¯.In formula (11), Lk represents the lead time for the kth level member in the supply chain to place an order with the k+1th level member and has the following formula:
(12)ut1¯=∑i=1nFt−1nutk¯=∑i=1n−1Qt−jk−1n,k≥2.In formula (12), u¯t1 represents the demand of first-level members, that is, customers in period t; u¯tk represents the demand of kth members in period t; n represents the number of observation periods of demand forecast Qt−jk−1; it represents the order quantity of the k−1th level member in tj period.Assuming thatQtk is used to represent the order quantity of the kth member in period t, combining Equations (5) and (7) can be derived to obtain the variance ratio between Qtk and the end customer demand Ft as
(13)VarQtkVarFt≥∏i=1k−11+2Lin+2Li2n2∀k.


## 2.3.2. Information Sharing

Assuming that the supply chain realizes information sharing, based on the observed value of the demand, the average value of the next layer is predicted. The requirements are(14)ut¯=∑i=1nFt−1n.Order pointytk has
(15)ytk=Lk•utk¯+ϕLkStk.For the end customer demandFt, the supply chain variance is as follows:
(16)VarQtkVarFt≥1+2∑i=1k=1Lin+2∑i=1k=1Li2n2∀k.


## 2.3.3. Comparative Analysis of No Information Sharing and Information Sharing

Through the discussion and analysis of the two situations of whether information sharing is implemented in the supply chain, it can be known that the impact of information sharing on the bullwhip effect is different [16]. Only according to the structure of (13) and (16) it can be seen that after information sharing in the supply chain, the bullwhip effect will be weakened. The following is a detailed analysis of the value of information sharing by combining (13) and (16).First, only the retailer closest to the final customer has demand information, and members of other levels at all levels can only predict demand information based on the order information of neighboring lower-level members. Because the demand on these orders has been modified and adjusted, resulting in a huge deviation from the actual customer demand, the ratio of its variance to the variance of customer demand is increasing exponentially [17]. Therefore, the lack of sharing of supply chain information will exacerbate the bullwhip effect.Second, after information sharing, members at every level of the supply chain can obtain the actual demand information of the end customer. Customer demand is not large, and the gap between the number of orders and customer demand will gradually widen. Therefore, the implementation of information sharing in the supply chain can effectively weaken the bullwhip effect.Use the two formulas (13) and (16) to select values for the three parameters of L, n, and k, respectively, calculate the data, then draw based on these data, and compare and explain in a more vivid way.LetLi=1, i=1,⋯,k, take k=1,4,and6, respectively, and plot according to the obtained data. Figure 4 shows the bullwhip effect of nonsharing and sharing of supply chain information [18].Figure 4
Comparison of the bullwhip effect without information sharing.


## 3. Data and Algorithm Protection and Standard Experiment

### 3.1. Pay Attention to the Research of Artificial Intelligence Algorithm-Related Legislation

#### 3.1.1. Clarify the Legal Status of Artificial Intelligence Data and Algorithms

As the primary productive force, science and technology will have an important impact on human production, lifestyle, and social structure [19, 20] and will also affect the form and mode of law to a large extent. To put artificial intelligence data and algorithms in a legal context for discussion, the definition of its legal status is particularly important. To clarify the definition of the status of artificial intelligence data and algorithms in the law, it is essential to formulate and improve relevant regulations on the legal level of artificial intelligence. Through the establishment and improvement of the corresponding legal system, the legal status of weak artificial intelligence and strong artificial intelligence shall be clearly stipulated, so as to provide basic guarantee for the research of artificial intelligence at the legal level [21, 22].To clarify the legal status of artificial intelligence data and algorithms, in addition to establishing and improving corresponding laws and regulations, it is also possible to consider implementing an artificial intelligence registration system [23, 24]. The strong artificial intelligence that meets the corresponding conditions is registered, and the difference in number and data constitutes the main basis for artificial intelligence identification. Economic and social life continues to move forward with the improvement of productivity, but the law cannot be changed at will because of the need to maintain its stability, which leads to the possibility that the law may not adapt to the pace of development of social life. The hysteresis of the law makes it impossible to cover all the conditions in social life, especially in the era of rapid development of intelligence; the hysteresis of the law will make it difficult for emerging things such as artificial intelligence to fully adapt to the current law. Therefore, we need to look forward to the future, adopt a slightly forward-looking vision, fully understand the necessity and feasibility of strong artificial intelligence as a legal subject, and actively explore systems that are compatible with legal issues related to artificial intelligence data and algorithms, in order to promote artificial intelligence. The healthy development of smart future lays the institutional foundation.


#### 3.1.2. The Rights and Boundaries of Artificial Intelligence

Based on the above analysis and conclusions, strong artificial intelligence may become a legal subject and should enjoy rights [25]. As a legal subject, strong artificial intelligence should first enjoy the right to be respected.


#### 3.1.3. The Protection of Personal Information Rights Is Increasingly Strengthened

With the vigorous development of information technology, the widespread application of artificial intelligence has also been supported by massive amounts of information and data [26]. To better protect personal information and privacy, it is necessary to seek a balance between the protection of personal information and privacy and the application of artificial intelligence, so that artificial intelligence can achieve maximum development without infringing on personal information and privacy [27].


### 3.2. Establish and Improve the Ethical Norm System of Artificial Intelligence

For the regulation and adjustment of artificial intelligence data and algorithms, ethical norms have a predetermined effect. As a superstructure, law is a reflection of economic and social life based on productivity, and its inherent hysteresis makes it unable to include all the conditions in social life. To establish and improve the ethical system of artificial intelligence data and algorithms, the fundamental principle of “people-oriented” should be established first. The emergence and development of science and technology should take the promotion of human progress as the purpose and the benefit of mankind as the goal. However, technology is a “double-edged sword.” It can not only provide humans with unprecedented convenience and enjoyment but also bring various risks and even devastating blows to humans. Especially in the era of artificial intelligence, which is developing rapidly, “people-oriented” must be regarded as the primary principle of artificial intelligence development. The establishment of the “people-oriented” principle in the development of artificial intelligence can be considered from the following two aspects. first, it cannot pose a threat to human security. Regardless of the stage of development of artificial intelligence, the original intention should be “serving for mankind.” The second is that the status and rights of mankind must not be violated. The development of artificial intelligence cannot shake the status of human beings. In the current situation, the ethical norm system of artificial intelligence should play its role to promote the harmonious development of humans and artificial intelligence.The establishment of a sound ethical system of artificial intelligence data and algorithms requires government intervention and supervision. If government supervision is not strong at this time, it is very likely to cause new types of things such as artificial intelligence to be in an unreliable state, which will further increase people’s “vigilance” of artificial intelligence, which is not conducive to the development of artificial intelligence-related research and protection of human self-interests. Therefore, through the establishment of artificial intelligence ethics committees and other institutions, specializing in the ethics and supervision of artificial intelligence, cross-domain monitoring and management of the application of artificial intelligence can be carried out to ensure that the sound and orderly development of artificial intelligence can be promoted in all directions.


## 3.1. Pay Attention to the Research of Artificial Intelligence Algorithm-Related Legislation

### 3.1.1. Clarify the Legal Status of Artificial Intelligence Data and Algorithms

As the primary productive force, science and technology will have an important impact on human production, lifestyle, and social structure [19, 20] and will also affect the form and mode of law to a large extent. To put artificial intelligence data and algorithms in a legal context for discussion, the definition of its legal status is particularly important. To clarify the definition of the status of artificial intelligence data and algorithms in the law, it is essential to formulate and improve relevant regulations on the legal level of artificial intelligence. Through the establishment and improvement of the corresponding legal system, the legal status of weak artificial intelligence and strong artificial intelligence shall be clearly stipulated, so as to provide basic guarantee for the research of artificial intelligence at the legal level [21, 22].To clarify the legal status of artificial intelligence data and algorithms, in addition to establishing and improving corresponding laws and regulations, it is also possible to consider implementing an artificial intelligence registration system [23, 24]. The strong artificial intelligence that meets the corresponding conditions is registered, and the difference in number and data constitutes the main basis for artificial intelligence identification. Economic and social life continues to move forward with the improvement of productivity, but the law cannot be changed at will because of the need to maintain its stability, which leads to the possibility that the law may not adapt to the pace of development of social life. The hysteresis of the law makes it impossible to cover all the conditions in social life, especially in the era of rapid development of intelligence; the hysteresis of the law will make it difficult for emerging things such as artificial intelligence to fully adapt to the current law. Therefore, we need to look forward to the future, adopt a slightly forward-looking vision, fully understand the necessity and feasibility of strong artificial intelligence as a legal subject, and actively explore systems that are compatible with legal issues related to artificial intelligence data and algorithms, in order to promote artificial intelligence. The healthy development of smart future lays the institutional foundation.


### 3.1.2. The Rights and Boundaries of Artificial Intelligence

Based on the above analysis and conclusions, strong artificial intelligence may become a legal subject and should enjoy rights [25]. As a legal subject, strong artificial intelligence should first enjoy the right to be respected.


### 3.1.3. The Protection of Personal Information Rights Is Increasingly Strengthened

With the vigorous development of information technology, the widespread application of artificial intelligence has also been supported by massive amounts of information and data [26]. To better protect personal information and privacy, it is necessary to seek a balance between the protection of personal information and privacy and the application of artificial intelligence, so that artificial intelligence can achieve maximum development without infringing on personal information and privacy [27].


## 3.1.1. Clarify the Legal Status of Artificial Intelligence Data and Algorithms

As the primary productive force, science and technology will have an important impact on human production, lifestyle, and social structure [19, 20] and will also affect the form and mode of law to a large extent. To put artificial intelligence data and algorithms in a legal context for discussion, the definition of its legal status is particularly important. To clarify the definition of the status of artificial intelligence data and algorithms in the law, it is essential to formulate and improve relevant regulations on the legal level of artificial intelligence. Through the establishment and improvement of the corresponding legal system, the legal status of weak artificial intelligence and strong artificial intelligence shall be clearly stipulated, so as to provide basic guarantee for the research of artificial intelligence at the legal level [21, 22].To clarify the legal status of artificial intelligence data and algorithms, in addition to establishing and improving corresponding laws and regulations, it is also possible to consider implementing an artificial intelligence registration system [23, 24]. The strong artificial intelligence that meets the corresponding conditions is registered, and the difference in number and data constitutes the main basis for artificial intelligence identification. Economic and social life continues to move forward with the improvement of productivity, but the law cannot be changed at will because of the need to maintain its stability, which leads to the possibility that the law may not adapt to the pace of development of social life. The hysteresis of the law makes it impossible to cover all the conditions in social life, especially in the era of rapid development of intelligence; the hysteresis of the law will make it difficult for emerging things such as artificial intelligence to fully adapt to the current law. Therefore, we need to look forward to the future, adopt a slightly forward-looking vision, fully understand the necessity and feasibility of strong artificial intelligence as a legal subject, and actively explore systems that are compatible with legal issues related to artificial intelligence data and algorithms, in order to promote artificial intelligence. The healthy development of smart future lays the institutional foundation.


## 3.1.2. The Rights and Boundaries of Artificial Intelligence

Based on the above analysis and conclusions, strong artificial intelligence may become a legal subject and should enjoy rights [25]. As a legal subject, strong artificial intelligence should first enjoy the right to be respected.


## 3.1.3. The Protection of Personal Information Rights Is Increasingly Strengthened

With the vigorous development of information technology, the widespread application of artificial intelligence has also been supported by massive amounts of information and data [26]. To better protect personal information and privacy, it is necessary to seek a balance between the protection of personal information and privacy and the application of artificial intelligence, so that artificial intelligence can achieve maximum development without infringing on personal information and privacy [27].


## 3.2. Establish and Improve the Ethical Norm System of Artificial Intelligence

For the regulation and adjustment of artificial intelligence data and algorithms, ethical norms have a predetermined effect. As a superstructure, law is a reflection of economic and social life based on productivity, and its inherent hysteresis makes it unable to include all the conditions in social life. To establish and improve the ethical system of artificial intelligence data and algorithms, the fundamental principle of “people-oriented” should be established first. The emergence and development of science and technology should take the promotion of human progress as the purpose and the benefit of mankind as the goal. However, technology is a “double-edged sword.” It can not only provide humans with unprecedented convenience and enjoyment but also bring various risks and even devastating blows to humans. Especially in the era of artificial intelligence, which is developing rapidly, “people-oriented” must be regarded as the primary principle of artificial intelligence development. The establishment of the “people-oriented” principle in the development of artificial intelligence can be considered from the following two aspects. first, it cannot pose a threat to human security. Regardless of the stage of development of artificial intelligence, the original intention should be “serving for mankind.” The second is that the status and rights of mankind must not be violated. The development of artificial intelligence cannot shake the status of human beings. In the current situation, the ethical norm system of artificial intelligence should play its role to promote the harmonious development of humans and artificial intelligence.The establishment of a sound ethical system of artificial intelligence data and algorithms requires government intervention and supervision. If government supervision is not strong at this time, it is very likely to cause new types of things such as artificial intelligence to be in an unreliable state, which will further increase people’s “vigilance” of artificial intelligence, which is not conducive to the development of artificial intelligence-related research and protection of human self-interests. Therefore, through the establishment of artificial intelligence ethics committees and other institutions, specializing in the ethics and supervision of artificial intelligence, cross-domain monitoring and management of the application of artificial intelligence can be carried out to ensure that the sound and orderly development of artificial intelligence can be promoted in all directions.


## 4. Performance Analysis of the Program

### 4.1. Analysis of the Calculation Amount of the Scheme

According to the above scheme process, this section will analyze the calculation amount of the algorithm from the three major steps of shared secret distribution, shadow secret verification, and secret share reconstruction. The distribution and reconstruction of the secret share are based on the IBE, and then the threshold is used to control the security level of the algorithm. As shown in Table1, the calculation amount of the scheme is mainly concentrated in the two-wire pair operation in IBE.Table 1
Cloud computing privacy protection scheme based on secret sharing.
OperatingSecret distributionShare verificationSecret reconstructioneˆ calculation3m+k2kHash calculation210Lagrange interpolation001Modular exponent operation0k+1k−1Note:m is the number of secret distribution reservations, and k is the threshold.In addition, the shared secret distribution process includes Alice, the data provider, calculating all shadow secrets used for signature authentication, thus occupying part of the resources of shared secret distribution. The occupancy is shown in Table2, but considering that in practical application, the security parameter generator can configure more system resources in the IAM SYSTEM and can undertake a large number of computing tasks, so it will not cause significant time and performance consumption on cloud computing platform.Table 2
Secret distribution process calculation volume breakdown.
OperatingStep 1: generate secret sharesStep 2: generate shadow secreteˆ calculationm2m+kHash calculation11Lagrange interpolation00Modular exponent operation00Note:m is the number of secret distribution reservations, and k is the threshold.


### 4.2. Experimental Simulation Performance Analysis

#### 4.2.1. Statistics and Analysis of Experimental Simulation Data

As shown in Figure5, under the condition of a certain number of secret distributions, the time consumed for calculation increases with the increase of the threshold and shows a linear change. Therefore, it can be considered that the time consumed in the secret distribution phase is positively correlated with the set threshold.Figure 5
The secret distribution stage takes the threshold valuek as the dimension.As shown in Figure6, under the condition of a certain secret threshold, the time consumed for calculation increases with the increase in the number of secrets distributed, so it is found that the time consumed in the secret distribution phase is positively correlated with the number of secret distributions set.Figure 6
The secret distribution stage takes the distribution quantitym as the dimension.As shown in Figure7, when a certain secret distribution quantity m is specified, the time consumed by the verification process increases with the increase of the secret share threshold k and shows a linear change. Therefore, it can be determined that the time consumption of the verification phase is positively correlated with the secret share threshold k.Figure 7
The verification stage takes the threshold valuek as the dimension.As shown in Figure8, when a certain secret share threshold k is specified, the time consumption of the verification process does not change significantly with the increase of the secret distribution number m. So it can be considered that in the verification phase, the time consumption is not affected by the secret distribution quantity m.Figure 8
The verification phase takes the number of secret distributionm as the dimension.As shown in Figure9, when a certain secret distribution quantity m is specified, the time consumption of the secret reconstruction process increases with the increase of the secret share threshold k, and the increase is close to a linear change. Therefore, it can be determined that the time consumption of the secret reconstruction phase is positively correlated with the secret share threshold k.Figure 9
The reconstruction stage takes the threshold valuek as the dimension.The overall situation takes the threshold valuek as the dimension. As shown in Figure 10, when a certain secret share threshold k is specified, the time consumption of the secret reconstruction process does not change significantly with the increase in the number of secret distributions. Therefore, it can be determined that the time consumption of the secret reconstruction phase is not directly related to the number of secret distributions.Figure 10
The reconstruction phase takes the number of secret distributionm as the dimension.The overall situation takes the threshold valuek as the dimension. As shown in Figures 11 and 12, when a certain secret share threshold k is specified, the time consumption of the secret reconstruction process does not change significantly with the increase in the number of secret distributions. Therefore, it can be determined that the time consumption of the secret reconstruction phase is not directly related to the number of secret distributions.Figure 11
The overall situation takes the threshold valuek as the dimension.Figure 12
The overall situation takes the number of secret distributionm as the dimension.


### 4.3. Strengthening Artificial Intelligence Protection

#### 4.3.1. Regulate the Research and Development of Artificial Intelligence

AI is gradually integrating into human life. In the design and development process, in addition to the relevant legislation, the artificial intelligence program designed and developed can be preexamined to check whether there is a malicious program in the design and development stage.


#### 4.3.2. Standardize the Intermediate Links of Artificial Intelligence

Artificial intelligence data and algorithms have been involved in our social life, but they will inevitably cause different degrees of damage to humans in the development process. Therefore, in addition to regulating the artificial intelligence design and development stage, it is also necessary to regulate artificial intelligence. Intermediate links such as intelligent production, sales, and use are constrained. In order to limit the intermediate links of artificial intelligence, an identification code similar to human fingerprints can be set for artificial intelligence, and tracking procedures can be set inside artificial intelligence, and real-time monitoring of it when necessary, reducing and preventing artificial intelligence from bringing to humans. Harm can also prevent artificial intelligence from getting out of control during the development process.


## 4.1. Analysis of the Calculation Amount of the Scheme

According to the above scheme process, this section will analyze the calculation amount of the algorithm from the three major steps of shared secret distribution, shadow secret verification, and secret share reconstruction. The distribution and reconstruction of the secret share are based on the IBE, and then the threshold is used to control the security level of the algorithm. As shown in Table1, the calculation amount of the scheme is mainly concentrated in the two-wire pair operation in IBE.Table 1
Cloud computing privacy protection scheme based on secret sharing.
OperatingSecret distributionShare verificationSecret reconstructioneˆ calculation3m+k2kHash calculation210Lagrange interpolation001Modular exponent operation0k+1k−1Note:m is the number of secret distribution reservations, and k is the threshold.In addition, the shared secret distribution process includes Alice, the data provider, calculating all shadow secrets used for signature authentication, thus occupying part of the resources of shared secret distribution. The occupancy is shown in Table2, but considering that in practical application, the security parameter generator can configure more system resources in the IAM SYSTEM and can undertake a large number of computing tasks, so it will not cause significant time and performance consumption on cloud computing platform.Table 2
Secret distribution process calculation volume breakdown.
OperatingStep 1: generate secret sharesStep 2: generate shadow secreteˆ calculationm2m+kHash calculation11Lagrange interpolation00Modular exponent operation00Note:m is the number of secret distribution reservations, and k is the threshold.


## 4.2. Experimental Simulation Performance Analysis

### 4.2.1. Statistics and Analysis of Experimental Simulation Data

As shown in Figure5, under the condition of a certain number of secret distributions, the time consumed for calculation increases with the increase of the threshold and shows a linear change. Therefore, it can be considered that the time consumed in the secret distribution phase is positively correlated with the set threshold.Figure 5
The secret distribution stage takes the threshold valuek as the dimension.As shown in Figure6, under the condition of a certain secret threshold, the time consumed for calculation increases with the increase in the number of secrets distributed, so it is found that the time consumed in the secret distribution phase is positively correlated with the number of secret distributions set.Figure 6
The secret distribution stage takes the distribution quantitym as the dimension.As shown in Figure7, when a certain secret distribution quantity m is specified, the time consumed by the verification process increases with the increase of the secret share threshold k and shows a linear change. Therefore, it can be determined that the time consumption of the verification phase is positively correlated with the secret share threshold k.Figure 7
The verification stage takes the threshold valuek as the dimension.As shown in Figure8, when a certain secret share threshold k is specified, the time consumption of the verification process does not change significantly with the increase of the secret distribution number m. So it can be considered that in the verification phase, the time consumption is not affected by the secret distribution quantity m.Figure 8
The verification phase takes the number of secret distributionm as the dimension.As shown in Figure9, when a certain secret distribution quantity m is specified, the time consumption of the secret reconstruction process increases with the increase of the secret share threshold k, and the increase is close to a linear change. Therefore, it can be determined that the time consumption of the secret reconstruction phase is positively correlated with the secret share threshold k.Figure 9
The reconstruction stage takes the threshold valuek as the dimension.The overall situation takes the threshold valuek as the dimension. As shown in Figure 10, when a certain secret share threshold k is specified, the time consumption of the secret reconstruction process does not change significantly with the increase in the number of secret distributions. Therefore, it can be determined that the time consumption of the secret reconstruction phase is not directly related to the number of secret distributions.Figure 10
The reconstruction phase takes the number of secret distributionm as the dimension.The overall situation takes the threshold valuek as the dimension. As shown in Figures 11 and 12, when a certain secret share threshold k is specified, the time consumption of the secret reconstruction process does not change significantly with the increase in the number of secret distributions. Therefore, it can be determined that the time consumption of the secret reconstruction phase is not directly related to the number of secret distributions.Figure 11
The overall situation takes the threshold valuek as the dimension.Figure 12
The overall situation takes the number of secret distributionm as the dimension.


## 4.2.1. Statistics and Analysis of Experimental Simulation Data

As shown in Figure5, under the condition of a certain number of secret distributions, the time consumed for calculation increases with the increase of the threshold and shows a linear change. Therefore, it can be considered that the time consumed in the secret distribution phase is positively correlated with the set threshold.Figure 5
The secret distribution stage takes the threshold valuek as the dimension.As shown in Figure6, under the condition of a certain secret threshold, the time consumed for calculation increases with the increase in the number of secrets distributed, so it is found that the time consumed in the secret distribution phase is positively correlated with the number of secret distributions set.Figure 6
The secret distribution stage takes the distribution quantitym as the dimension.As shown in Figure7, when a certain secret distribution quantity m is specified, the time consumed by the verification process increases with the increase of the secret share threshold k and shows a linear change. Therefore, it can be determined that the time consumption of the verification phase is positively correlated with the secret share threshold k.Figure 7
The verification stage takes the threshold valuek as the dimension.As shown in Figure8, when a certain secret share threshold k is specified, the time consumption of the verification process does not change significantly with the increase of the secret distribution number m. So it can be considered that in the verification phase, the time consumption is not affected by the secret distribution quantity m.Figure 8
The verification phase takes the number of secret distributionm as the dimension.As shown in Figure9, when a certain secret distribution quantity m is specified, the time consumption of the secret reconstruction process increases with the increase of the secret share threshold k, and the increase is close to a linear change. Therefore, it can be determined that the time consumption of the secret reconstruction phase is positively correlated with the secret share threshold k.Figure 9
The reconstruction stage takes the threshold valuek as the dimension.The overall situation takes the threshold valuek as the dimension. As shown in Figure 10, when a certain secret share threshold k is specified, the time consumption of the secret reconstruction process does not change significantly with the increase in the number of secret distributions. Therefore, it can be determined that the time consumption of the secret reconstruction phase is not directly related to the number of secret distributions.Figure 10
The reconstruction phase takes the number of secret distributionm as the dimension.The overall situation takes the threshold valuek as the dimension. As shown in Figures 11 and 12, when a certain secret share threshold k is specified, the time consumption of the secret reconstruction process does not change significantly with the increase in the number of secret distributions. Therefore, it can be determined that the time consumption of the secret reconstruction phase is not directly related to the number of secret distributions.Figure 11
The overall situation takes the threshold valuek as the dimension.Figure 12
The overall situation takes the number of secret distributionm as the dimension.


## 4.3. Strengthening Artificial Intelligence Protection

### 4.3.1. Regulate the Research and Development of Artificial Intelligence

AI is gradually integrating into human life. In the design and development process, in addition to the relevant legislation, the artificial intelligence program designed and developed can be preexamined to check whether there is a malicious program in the design and development stage.


### 4.3.2. Standardize the Intermediate Links of Artificial Intelligence

Artificial intelligence data and algorithms have been involved in our social life, but they will inevitably cause different degrees of damage to humans in the development process. Therefore, in addition to regulating the artificial intelligence design and development stage, it is also necessary to regulate artificial intelligence. Intermediate links such as intelligent production, sales, and use are constrained. In order to limit the intermediate links of artificial intelligence, an identification code similar to human fingerprints can be set for artificial intelligence, and tracking procedures can be set inside artificial intelligence, and real-time monitoring of it when necessary, reducing and preventing artificial intelligence from bringing to humans. Harm can also prevent artificial intelligence from getting out of control during the development process.


## 4.3.1. Regulate the Research and Development of Artificial Intelligence

AI is gradually integrating into human life. In the design and development process, in addition to the relevant legislation, the artificial intelligence program designed and developed can be preexamined to check whether there is a malicious program in the design and development stage.


## 4.3.2. Standardize the Intermediate Links of Artificial Intelligence

Artificial intelligence data and algorithms have been involved in our social life, but they will inevitably cause different degrees of damage to humans in the development process. Therefore, in addition to regulating the artificial intelligence design and development stage, it is also necessary to regulate artificial intelligence. Intermediate links such as intelligent production, sales, and use are constrained. In order to limit the intermediate links of artificial intelligence, an identification code similar to human fingerprints can be set for artificial intelligence, and tracking procedures can be set inside artificial intelligence, and real-time monitoring of it when necessary, reducing and preventing artificial intelligence from bringing to humans. Harm can also prevent artificial intelligence from getting out of control during the development process.


## 5. Conclusions

Since the law is often the product of compromise between the interests of all parties, artificial intelligence, as a new legal thing, needs to be included in the scope of legal adjustment. Therefore, from the perspective of resource sharing, the use of legal means to protect artificial intelligence is beneficial to promoting the development of the artificial intelligence industry and can better regulate its relationship with other legal subjects or objects. The development of AI is still in its infancy. Whether it is the law or other social groups, it should be given more room for tolerance and growth, and a reasonable top-level design should be given to it. This article mainly discusses the research of artificial intelligence data and algorithms in the legal context, in which the definition of legal status is particularly important. But to clarify the status of artificial intelligence data and algorithms in the law, it is necessary to formulate and improve related laws and regulations. On the legal level of artificial intelligence, by establishing and improving the corresponding legal system, the legal status of weak and strong artificial intelligence has been clearly stipulated to provide basic guarantee for its research.



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*Source: 8601425-2021-08-26.xml*