WO2022001535A1 - Fog-based multi-dimensional multi-angle electricity consumption data aggregating system - Google Patents

Abstract

A fog-based multi-dimensional multi-angle electricity consumption data aggregating system. According to types of electric appliances of each user and energy efficiency index information of the electric appliances, a control center allocates, to each electric meter, a set of super-increasing sequences matching the electric meter, the electric meter can acquire the user's electricity consumption information comprising the types of electric appliances and the energy efficiency indexes of the electric appliances, and the electricity consumption information is encrypted by means of the allocated super-increasing sequences and then sent to a fog node; and the fog node aggregates data and then sends same to the control center, and the control center obtains multi-dimensional multi-angle electricity consumption data of a region for which the control center is responsible. Compared with the current multi-dimensional data aggregating solution in which the acquired electricity consumption data is only precise to the type of electric appliances, the precision of electricity consumption data collected by the control center is improved, and more microscopic data can be calculated. Moreover, the problem of all the user data being recoverable as long as a third party obtains a set of super-increasing sequences is also solved, thereby improving the security.

Description

A fog-based multi-dimensional and multi-angle electricity data aggregation system technical field

The invention relates to the technical field of power information security, in particular to a fog-based aggregation system for multi-dimensional and multi-angle power consumption data.

Background technique

To ensure intelligent load balancing between production and demand, many countries and regions are actively deploying electricity meters. Electricity meters regularly measure and report power consumption in real time, a feature that helps utility providers better monitor, control and predict power consumption. Utility providers can analyze electricity consumption data to implement tiered tariffs and dynamically update their prices, while increasing or decreasing production based on demand to implement demand-side management. In addition, fine-grained electricity usage data can help analyze consumer energy consumption behavior, demand response optimization, and improve energy-saving recommendations. While electricity meters offer some obvious benefits, accurate and fine-grained measurement of home energy consumption raises serious privacy concerns. In this regard, fine-grained data on user electricity usage can reveal whether users are at home, the appliances they use in real time and their characteristics, and even their daily habits at home. Based on these real-time data reflecting user activities, malicious attackers can use to analyze users' private habits, thieves may break in when the house is empty, which can lead to serious consequences.

Since the use of electricity meters is critical for better supply and demand management in smart grids, how to strike a balance between the availability and confidentiality of electricity consumption data is also a crucial academic issue. To address this problem, privacy-preserving data aggregation can be a viable solution, where an aggregation unit periodically aggregates the electricity usage of a group of users in a geographic area, and utility providers can obtain an overview of the electricity usage data in the area. The sum, but nothing is known about the individual power usage in the area. The technical tools currently used to protect the privacy of fine-grained data aggregation include homomorphic encryption, differential privacy, and adding masks. In recent years, some researchers have proposed multi-dimensional data aggregation schemes. For example, Lu et al. proposed an effective, privacy-preserving aggregation scheme (EPPA), which uses super-increasing sequences to construct multi-dimensional data and uses homomorphism. Paillier encryption technology encrypts structured data, this scheme enables smart meters to report data of multiple electrical appliance types in one report message, this scheme also supports mutual communication between entities, and the local gateway directly aggregates the ciphertext data, Without decryption, the control center can obtain the aggregated results of the original data. However, since all users use the same ciphertext, as long as the key and a set of super-increasing sequences are obtained, the power consumption data of all users can be recovered, and the security factor is low. Yang et al. proposed a multi-subset-based multi-dimensional data aggregation scheme, which sets the user's power consumption data as a multi-dimensional data set, divides users into multiple subsets according to power consumption, and adds a blind factor to confuse the real power consumption data. And use homomorphic encryption technology to encrypt the user's power consumption information. This scheme can calculate the number of users in each subset and the sum of electricity consumption per dimension of all users, but this scheme does not consider the problem of meter failure. The advantage of multi-dimensional aggregation is its classification of aggregated powered devices. Multi-dimensional data aggregation can complete the aggregation of two or more types of data, and classify the electricity consumption of different types of electrical appliances in the user's home and upload it to the control center. After the control center obtains the data, it can analyze the data of the user's different electrical appliances to complete the function. Fine-grained analysis. Now with the development of smart grid and the application of new energy technologies, the requirements for finer granularity and security of users' electricity consumption data are getting higher and higher. In the current research, the fine-grained data collected by electricity meters is only accurate to the type of electrical appliance, without taking into account more Microscopic data, for example, air conditioners can be classified into energy efficiency grades 1/2/3 according to the Chinese inverter air conditioner energy efficiency standard GB21455-2013. Analyzing this fine-grained data can help prevent large-scale coordinated attacks by Internet of Things (IoT) botnets composed of high-powered devices.

At the same time, since users have higher expectations for network performance and service quality in the era of big data, when faced with a large number of reports and queries collected from users, the current traditional cloud computing faces obvious challenges in terms of computing power and storage. Insufficient, has been unable to meet the growing requirements of privacy protection and communication bandwidth. Fog computing offers many advantages over cloud computing, such as low latency and fast response, strong location awareness, and enhanced reliability and security. These advantages have facilitated the emergence of fog-based smart grids, where the use of aggregation capabilities in fog nodes (FNs) prevents utility providers from obtaining a single ciphertext through a reliable communication network deployed in parallel with the transmission and distribution grid, and aggregated ciphertexts can be combined. The text is provided to the Control Center (CC), as shown in Figure 1.

Therefore, in the existing scheme, the aggregated data is only accurate to the type of electrical appliance, and does not take into account the more microscopic data. Moreover, in the existing methods based on multi-dimensional data aggregation, such as the EPPA scheme, all users use the same super-increasing sequence. As long as the attacker obtains a set of super-increasing sequences, the power consumption data of all users can be recovered. would raise serious privacy concerns.

SUMMARY OF THE INVENTION

The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a fog-based multi-dimensional and multi-angle power consumption data aggregation system, including a control center and several fog nodes connected to the control center. The coverage of the control center is divided into several sub-areas. There is one fog node in the area, and each fog node is connected to several electricity meters within its coverage area;

The control center generates a corresponding set of super-increasing sequences for each electric meter based on the homomorphic Paillier encryption method according to the types of electrical appliances within the coverage of each electric meter and its energy efficiency level information, and sends the super-increasing sequences to the corresponding electricity meter;

Each electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, and encrypts the electricity consumption data using the super-increasing sequence to generate ciphertext, and encapsulates the ciphertext into an electricity consumption message and sends it to the corresponding fog node;

Each fog node aggregates all received ciphertexts to obtain aggregated data, and encapsulates the aggregated data into aggregated messages and sends them to the control center;

The control center decrypts all the received aggregated data, and obtains electricity consumption data including the types of electrical appliances and their energy efficiency levels within the coverage area of the control center.

According to the embodiments of the present invention, at least the following beneficial effects are obtained:

Compared with the existing multi-dimensional data aggregation scheme, the electricity consumption data collected is only accurate to the type of electrical appliances. In order to improve the accuracy of the power consumption data collected by the control center, more microscopic data can be counted. At the same time, it also solves the problem that the third party can restore all user data as long as it obtains a set of super-increasing sequences, which significantly improves the security of power consumption data transmission.

According to some embodiments of the present invention, before the control center generates the super-incrementing sequence, each electricity meter and fog node submit an identity verification message to the control center, and the control center verifies the identity verification message.

According to some embodiments of the present invention, each meter and fog node submit an identity verification message to the control center, and the control center verifies the identity verification message, specifically including:

The electricity meter generates a first random number, and generates a first digital signature according to the first random number, the virtual identity and the first key, and encapsulates the virtual identity, the first digital signature and the first random number into an authentication message and passes through the corresponding fog. The node sends the control center, wherein the virtual identity is allocated by the control center for identification of the electric meter; the first key is the private key allocated by the control center to the electric meter;

The fog node generates a second random number, generates a second digital signature according to the identity label, the second random number and the first shared key, and encapsulates the identity label, the second digital signature and the second random number into a message and sends it to the control center, where , the identity label is allocated by the control center and used for identification of the fog node; the first shared key is allocated by the control center as a shared key between the corresponding fog node and the control center;

The control center verifies the identity of the meter according to the virtual identity and the first digital signature, and verifies the identity of the fog node according to the second digital signature.

According to some embodiments of the present invention, each electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, and uses the super-incrementing sequence to encrypt the electricity consumption data to generate ciphertext, and encapsulates the ciphertext into an electricity consumption message. Send to the corresponding fog node, including:

The electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, generates a third random number, encrypts the collected electricity consumption data according to the third random number and the super-increasing sequence, and generates a ciphertext;

The electricity meter generates a third digital signature according to the ciphertext, the second shared key and the current timestamp, wherein the second shared key is allocated by the control center as a shared key between the corresponding electricity meter and the fog node;

The electricity meter selects a temporary identity, encapsulates the temporary identity, ciphertext, current time stamp and third digital signature into a electricity consumption message and sends it to the corresponding fog node, wherein the fog node assigns a set of temporary identities to the electricity meter each time;

The fog node verifies the temporary identity, the current timestamp and the third digital signature, and if the verification is successful, the received data is retained.

According to some embodiments of the present invention, after each temporary identity is used by the electricity meter, the temporary identity is deleted, and when all temporary identities are deleted, the corresponding fog node is requested to reassign a set of temporary identities.

According to some embodiments of the present invention, each fog node aggregates all received ciphertexts to obtain aggregated data, and encapsulates the aggregated data into an aggregated message and sends it to the control center, specifically including:

The fog node aggregates all received ciphertexts to obtain aggregated data;

The fog node generates a fourth digital signature according to the identity tag, aggregated data, first shared key and current timestamp;

The fog node encapsulates the identity tag, aggregated data, current timestamp and fourth digital signature into an aggregated message and sends it to the control center;

The control center verifies the current time stamp and the fourth digital signature, and if the verification is successful, the received data is retained.

According to some embodiments of the present invention, after each time the control center verifies the identity verification message, it reassigns a new virtual identity to each electric meter.

Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:

1 is a schematic diagram of a cloud-fog-based smart grid architecture provided by the prior art;

2 is a schematic structural diagram of a fog-based multi-dimensional and multi-angle electricity data aggregation system provided by an embodiment of the present invention;

3 is a schematic flowchart of initialization provided by an embodiment of the present invention;

4 is a schematic diagram of generating a super-incrementing sequence provided by an embodiment of the present invention;

5 is a schematic flowchart of data collection, data aggregation, and data extraction provided by an embodiment of the present invention;

6 is an experimental comparison diagram provided by an embodiment of the present invention;

7 is an experimental comparison diagram provided by an embodiment of the present invention;

8 is an experimental comparison diagram provided by an embodiment of the present invention;

9 is an experimental comparison diagram provided by an embodiment of the present invention;

10 is an experimental comparison diagram provided by an embodiment of the present invention;

FIG. 11 is an experimental comparison diagram provided by an embodiment of the present invention.

detailed description

The following describes in detail the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, only used to explain the present invention, and should not be construed as a limitation of the present invention.

First, some terms in the present invention are briefly explained;

(1) Homomorphic Paillier encryption technology;

Key generation: given a security parameter k ₁ by the control center (CC), at first select two large prime numbers p ₁ , q ₁ , set |p ₁ |=|q ₁ |=k _{1 to} calculate N=p ₁ q ₁ and λ=lcm(p ₁ -1, q ₁ -1). define a function

Then choose a generator

Calculate μ=(L(g ^λ mod N ² )) ^-1 mod N again. Finally, the public key pK=(N, g) is released to all electricity meters, and the private key SK=(λ, μ) is reserved.

Meter encryption: the meter obtains the meter data

pick a random number

Then the ciphertext can be calculated as c=g ^m ·

in

is a set of positive integers.

Control center decryption: use the private key (λ, μ) to decrypt c to get

(2) Types of electrical appliances and their energy efficiency grades:

Type of appliance: For example, air conditioners, washing machines, and televisions are each called a category of appliances.

Energy efficiency grades: Electrical appliances have different energy efficiency grades. Among them, the energy efficiency grades of each electrical appliance have a unified standard. For example, air conditioners can be divided into energy efficiency grades 1/2/3 according to China's energy efficiency standard for inverter air conditioners GB21455-2013. Appliances such as air conditioners have three energy efficiency classes.

It should be noted that the type of electrical appliances in the user covered by each electricity meter and their energy efficiency level information have been known by the corresponding electricity meter and control center. The information of the energy efficiency level of the machine has been known by the corresponding electricity meter and control center. This is within the scope of information collection and is not covered in this article.

2 to 5, an embodiment of the present invention provides a fog-based multi-dimensional and multi-angle electricity data aggregation system, including: control center, fog node and electricity meter three types of entities, the coverage of the control center It is divided into several sub-areas, each sub-area contains a fog node, and the coverage area of each fog node contains several electricity meters, specifically:

The system can be divided into cloud layer, fog layer and user layer. The cloud layer includes a utility provider with a control center (CC), and the fog layer includes several fog nodes (FN) with communication, aggregation and computing functions. The layer is a number of user Home Local Area Networks (HANs) within the coverage area of a certain fog node (each HAN contains a meter (SM)). The system is shown in Figure 2:

Control Center: Responsible for collecting, processing and analyzing real-time meter data, and issuing grid commands to fog nodes and meters to provide reliable services for the smart grid.

Fog Nodes: Fog nodes store, process, and relay the flow of information between the control center and the meter, including grid commands, requests, and meter readings.

Electricity meters: Electricity meters can collect electricity consumption data in real time, and send their service requests and data statistics to the control center through fog nodes.

The system mainly performs four operations: initialization, data collection, data aggregation and data extraction, as follows:

First, initialization;

The control center generates a set of super-increasing sequences for each electric meter based on the homomorphic Paillier encryption method according to the types of electrical appliances within the coverage area of each electric meter and its energy efficiency level information, and sends the super-increasing sequences to the corresponding electric meters.

In order to prevent dishonest or fake meters from falsifying data, resulting in inaccurate aggregated results, as a preferred implementation, before the control center generates a super-increasing sequence, all meters and fog nodes submit identity verification to the control center information.

The specific details of initialization are:

(1) The electricity meter SM _i generates a random number R _s and calculates the digital signature

then send the message

to the corresponding fog node FN _j . in,

A virtual identity assigned by the control center to the meter SM _i before the initialization operation, through which the control center passes

Identify the meter SM _i ;

_{It is a private key assigned to the meter SM i} by the control center before the initialization operation; || is the connector, and h() represents a one-way hash function with a length of 160 bits.

(2) After the fog node FN _j receives the message M ₁ , it generates a random number R _f and calculates the digital signature

then send the message

to the control center. in,

An identity label assigned by the control center to the fog node FN _j before the initialization operation, the control center passes the identity label

Identify the fog node FN _j ;

It is allocated before the initialization operation for the control center, and is the shared key between the _{fog node FN j and the control center.}

(3) The control center will

Map to the user's real identity and electrical appliance type and its energy efficiency level, and calculate and verify V _i and V _j to determine whether the data has been maliciously modified. It should be noted that the full text uses the same hash operation by default.

(4) After the verification is successful, the control center generates a new virtual identity _{for the meter SM i}

With each initialization, the control center generates a new virtual identity _{for each meter SM i for added security.} As shown in Figure 3, the control center _{generates a set of data for the meter SM i} that matches the user's electrical appliance type and its energy efficiency level.

a ₁ ∈{1,2,…,m ₁ },a ₂ ∈{1,2,…,m ₂ },…, _al ∈{1,2,…,m _l }.

Control Center Computing

in,

is the real identity of the meter SM _i. The control center sends the message M ₃ : {a,b,c,d,V ₀ ,V ₁ } to the fog node FN _j .

The purpose of setting these parameters is to prevent fog nodes or electricity meters from directly obtaining each other's private parameters during the parameter transmission process, for example:

meter awareness

So the meter wants to get the parameters sent to the meter by the control center

can be calculated

The fog node does not know the parameters

So it can't be unlocked. Among them, ⊕ is the exclusive or operator.

The detailed process of generating a set of corresponding super-increasing sequences for each meter SM _{i by the control center is as follows:}

First, the control center uses the homomorphic Paillier encryption method to give two security parameters k ₁ , k ₂ , runs the parameters in Paillier to generate Gen(k ₂ ), and obtains the keys (g, μ, λ, p ₁ , q ₁ , N=p ₁ ·q ₁ ), let |p ₁ |=|q ₁ |=k ₁ , calculate the public key (N, g), and keep the private key (λ, μ). Assuming that _{the maximum number of meters managed by an FN j} does not exceed a constant n, there are l types of electrical appliances, and each type of electrical appliance has data of m _i energy efficiency levels:

m ₁ ,m ₂ ,...,m _l may or may not be equal, and each type of energy efficiency level data

) is less than a constant d. Assuming that P is the sum of the number of households using each type of appliances of each energy efficiency level within the coverage area _{of FN j} (for example: 20 households _{using D 1,1} appliances, 15 households _{using D 1,2 appliances,} …,use

There are 25 households with electrical appliances, then P=20+15+…+25).

Then, the control center selects a set of large prime numbers

Its length is |α _i,s |≥k ₂ ,s∈[1,m _i ],

is an increasing prime number, such as α _i,s-1 <α _i,s (s=2,...,m _i );

satisfy

It should be noted that, because the data of all electrical appliance types and their energy efficiency levels in a family can add up to dozens of pieces at most, and only a few dozen prime numbers are needed for one initialization, and there are 6,057 prime numbers within 60,000, so this Embodiments are possible.

Finally, the control center calculates

The generation process is shown in Figure 4.

(5) After the fog node FN _j receives M ₃ , it first calculates and verifies V ₀ , and then calculates after the verification is successful.

and

Fog node FN _j generates a set of temporary identities

and calculated

and

store temporary identity

and, the shared key between the _{meter SM i} and the fog node FN _j

shared key

Assigned to Control Center. in,

to express

Encrypted virtual identity

Fog node FN _j sends a message

Feed the meter SM _i .

(6) After the electricity meter SM _i receives the message M ₄ , it first calculates

And verify V ₁ , after successful calculation,

Then verify V ₂ , decrypt with kh _i

get

and store the above information.

When _{every SM i is} used up

delete the temporary identity

Next time choose one from the remaining temporary identities. when all temporary status

After all are used up, SM _i re-initiates a registration application to the control center, and then re-initializes the system to request the corresponding FN _{j to} re-assign a set of temporary identities.

In the process to (6) above (1), the control center of the message M ₂ by the virtual identity verification request

V _i and signature to authenticate the SM _i, which only legitimate SM _i to produce valid hash value of the output V _i. The control center authenticates FN _j with the parameter V _j , which must be equal to

Meanwhile, FN _j and SM _i use the response parameters V ₀ and V ₁ to verify the control center, respectively. In addition, a secure shared key is established between _{each SM i} and FN _j

To prevent dishonest or fake meters from falsifying data. Therein, it is assumed that the various entities (SM _i , FN _j and the control center) will not disclose their keys to anyone. In this regard, only knowing the key

The legal FN _j can be calculated

and

Similarly, only have the key

The real meter SM _i can calculate

Second, data collection;

Each electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, and encrypts the electricity consumption data with a super-increasing sequence to generate ciphertext, which is encapsulated into an electricity consumption message and sent to the corresponding fog node. The specific details are as follows:

(1) SM _i meter periodically (e.g., 15 minutes) acquired power data m _i1, m _i2 type comprising a collector and energy efficiency rating, ..., m _il, wherein each data m _ih ≤d (h = 1,2, ...,l), generate random numbers

The collected electricity consumption data is encrypted according to random numbers and super-increasing sequences to generate ciphertext

(2) The meter SM _i calculates a signature

t _i is the current timestamp;

(3) Meter SM _i selects a temporary identity

where x∈[1,q], and send the message

to the fog node FN _j ;

(4) The fog node FN _j checks and verifies the temporary identity of the _{meter SM i}

and locate the virtual identity of the meter

Check timestamp t _i , compute signature

Comparing S _i ' and S _i , when they are equal, the fog node FN _j receives and stores the message MS _i .

When _{every SM i is} used up

delete the temporary identity

Next time choose one from the remaining temporary identities. when all temporary status

After all are used up, SM _i re-initiates a registration application to the control center, and then re-initializes the system to request the corresponding FN _{j to} re-assign a set of temporary identities. SM _i is not allowed to use the same temporary identity twice when sending data

And the temporary identity is only known by FN _j , therefore, the attacker cannot guess whether the usage data of two consecutive sessions comes from the same SM _i , and this scheme is beneficial to prevent eavesdroppers from eavesdropping on privacy.

In this operation, FN _j checks whether the received data is the same as that sent by _{each smart meter SM i.} If an attacker has tampered with c _ji , when FN _j verifies

When inconsistencies are found, the message is judged to be false. Therefore, the attacker needs to tamper with S _i at the same time to deceive FN _j , but the attacker obtains

Computationally infeasible. And if the attacker tampered with t _i and

Likewise, since the attacker obtains a temporary identity

(where x∈[1,q]) is computationally infeasible when FN _j is verifying t _i and

When inconsistencies are found, the message is judged to be false. Therefore, even if the attacker tampered with the message MS _i , he could never fool FN _j . And FN _j authenticates each smart meter SM _i with the timestamp t _i and the signature S _i , which can identify any replay attacks performed by the attacker.

Since the usage data m _{ih of SM i} is encrypted and sent to FN _j through an open channel, the attacker can obtain the ciphertext c _ji . In order to satisfy the properties of homomorphic Paillier encryption from

Obtain m _ih (where a _i ∈ {1,2,…,m _i },i∈[1,l]), the attacker needs to use the private key (λ,μ) to decrypt c _ji first to get

and

need

to decrypt. Assuming the worst case, the private key (λ, μ) and

are obtained by the attacker, since each sequence

Even if the attacker obtains all the decryption keys of a certain user, he cannot decrypt the electricity usage privacy information of other users.

Third, data aggregation;

Each fog node aggregates all the received ciphertexts to obtain aggregated data, and encapsulates the aggregated data into aggregated messages and sends them to the control center. The specific details are as follows:

(1) After the fog node FN _j receives the messages sent by all the electricity meters within its coverage, it aggregates each ciphertext to obtain aggregated data C _j , where A _i,s (i=1,2,...,l;s =1,2,...,m _i ) is the collection of households using the appliances of the ith type and the sth energy efficiency class (for example, A _1,1 is the collection of households using the appliances of the 1st type and the 1st energy efficiency class):

(2) Fog node FN _j calculates the signature

t _j is the current timestamp;

(3) The fog node FN _j sends

to the control center;

(4) The control center checks the timestamp t _j and calculates the signature

_Sj ' and _{Sj are} then compared, and if they are equal, the control center receives and stores the message _MSj .

In the same way as the above analysis, the control center uses timestamp t _j and signature S _j to authenticate each fog node FN _j , which helps to detect any manipulation of the electricity aggregated data by the attacker during the communication process.

If the attacker to FN _j database, since the FN _j decrypts only data without the polymerization, the polymerization can also satisfy the data Paillier homomorphic encryption properties, and the above analysis Similarly, even if the attacker to FN _j for all encryption The ciphertext cannot be decrypted.

The control center can decrypt the data to obtain electricity consumption data including the type of electrical appliance and its equivalent level (that is, to obtain multi-dimensional and multi-angle electricity consumption data), if an attacker invades the control center, because the decryption parameters

Only the control center knows, and the attacker cannot get the parameters directly from the control center

So the aggregated ciphertext cannot be decrypted. In addition, even if the attacker obtains the final decryption result, since the data comes from multiple users, the attacker cannot identify a specific user, so the privacy of the user is guaranteed.

Fourth, data extraction;

The control center decrypts the aggregated data with the reserved private key (λ, μ), and obtains all the electricity consumption data including the type of electrical appliance and its energy efficiency level within its coverage area (that is, the multi-dimensional and multi-angle electricity consumption within its coverage area is obtained. data), the details are as follows:

(1) According to the aggregated data C _j :

(2) The control center decrypts the ciphertext:

make

So C=g ^D · R ^N modN ² can still be decrypted by the control center with the private key (λ, μ) to obtain D:

Further, since each energy efficiency level data of each type mentioned above is less than d:

Therefore:

in,

By analogy, the electricity consumption data of all electrical appliance types and their equivalent grades can be obtained

Compared with the existing multi-dimensional data aggregation solutions, the electricity consumption data collected is only accurate to the type of electrical appliances. This system collects electricity consumption data including electrical appliance types and their energy efficiency levels, such as

It realizes the collection of multi-dimensional and multi-angle electricity consumption data, improves the accuracy of electricity consumption data collection by the control center, and can count more microscopic data.

A fog-based multi-dimensional and multi-angle electricity data aggregation system provided by the embodiment of the present invention has the following beneficial effects:

(1) The control center assigns a set of matching super-increasing sequences to each electric meter according to the electrical appliance type and energy efficiency level information of each user. The center has obtained multi-dimensional and multi-angle electricity consumption data in its responsible area. Compared with the electricity consumption data collected in the existing multi-dimensional data aggregation scheme, the electricity consumption data is only accurate to the type of electrical appliances. This system improves the accuracy of electricity consumption data collection. , more microscopic data can be counted, and it is convenient for utility suppliers to dynamically obtain the overall power consumption in real time to implement demand-side management.

(2) Compared with the existing multi-dimensional data aggregation scheme, the control center of the system assigns a set of matching super-increasing sequences to each electric meter according to the electrical appliance type and energy efficiency level information of each user, which solves the problem in the current scheme. As long as the attacker obtains a set of super-increasing sequences, the data of all users can be recovered.

(3) Before the electricity meter collects the electricity consumption data, the fog nodes all perform identity authentication to the control center before aggregating the electricity consumption data, so as to prevent dishonest or false electricity meters from falsifying the data, resulting in inaccurate aggregation results.

(4) In this system, no electricity consumption data about the user is disclosed, and the security is guaranteed.

(5) Since the system does not require the participation of a third party (TTP), it does not need to perform complex operations such as bilinear pairing in the EPPA scheme, which reduces operational complexity and improves communication efficiency.

Referring to FIGS. 6 to 11, an embodiment of the present invention provides a set of simulation experiments comparisons between the present scheme and the EPPA and MMDAPP schemes, wherein the relevant content of the EPPA scheme can refer to the literature "R.Lu, X.Liang, X.Li , X.Lin and X.Shen,"EPPA:An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications,"in IEEE Transactions on Parallel and Distributed Systems,vol.23,no.9,pp.1621-1631, Sept.2012."; For the relevant content of the MMDAPP scheme, please refer to the literature "X.Yang,S.Zhang and B.Wang,"Multi-data Aggregation Scheme Based on Multiple Subsets to Realize User Privacy Protection,"2018 12th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID),Xiamen,China,pp.61-65,2018.”, the specific experimental results are as follows:

Assuming that there are 10 types of electrical appliances in the data, Figure 6 shows the relationship between the calculation cost of the electricity meter and the number of electrical appliance types. Obviously, compared with the EPPA and MMDAPP schemes, the calculation overhead of the electricity meter in this scheme is significantly reduced. On the other hand, the relationship between the computational overhead of fog nodes and the number of users is shown in Figure 7. Compared with the EPPA and MMDAPP schemes, the slope of the computational overhead curve of the fog nodes in this scheme is lower. When the number of users is n=200, 400, 600, 800, and 1000, this scheme saves 448.65ms, 890.65ms, 1332.65ms, 1774.65ms, and 2216.65ms of computational overhead in turn compared with the EPPA and MMDAPP schemes. Typically, if the computational overhead is too high, limitations in computing power and frequency can cause data delays and other failures. Therefore, compared with the EPPA and MMDAPP schemes, this scheme is undoubtedly more suitable for data aggregation in the smart grid.

Suppose there are 10 types of electrical appliances in the data, and the number of equivalent grades for each of the first five electrical appliance types is 3, and the equivalent grade of each electrical appliance type in the last five electrical appliance types is 5. Figure 8 shows a comparison of the fine-grained data between this scheme and the EPPA and MMDAPP schemes. Obviously, the data obtained in the EPPA and MMDAPP schemes can only be accurate to a variety of electrical appliance types, while the data obtained in this scheme can not only be accurate to a variety of electrical appliance types, but also accurate to multiple equivalent levels of each electrical appliance type. Therefore, the data obtained by this program are more detailed and have more analytical value.

Assuming that the number of users is n=1, and the number of electrical appliance types is 10, the communication overhead between the electricity meter and the fog node, and between the fog node and the control center is shown in Figure 9 and Figure 10, respectively. It can be clearly seen from Figure 9 and Figure 10 that the communication overhead between the electricity meter and the fog node and the communication overhead between the fog node and the control center in this scheme are smaller than those of the EPPA and MMDAPP schemes, and the size of the communication overhead is different from the type of electrical appliance. Number doesn't matter.

Assuming that the number of user electricity meters is n=200, 400, 600, 800, 1000, during the communication between electricity meters and fog nodes, this scheme saves 6400 bytes, 12800 bytes, 19200 bytes, 25600 bytes, and 32000 words in turn compared with EPPA and MMDAPP schemes. section bandwidth. The total communication overhead between the meter and the fog node is shown in Figure 11.

As can be seen from Figure 11, compared with the EPPA and MMDAPP schemes, the communication overhead of this scheme is lower. More importantly, compared with the EPPA and MMDAPP schemes, the data obtained by this scheme can not only be accurate to a variety of electrical appliance types, but also accurate to multiple equivalent levels of each electrical appliance type. To sum up, the solution effectively reduces the communication cost and improves the communication efficiency.

Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, The scope of the invention is defined by the claims and their equivalents.

Claims (7)

1. A fog-based multi-dimensional and multi-angle electricity data aggregation system, characterized in that it includes a control center and several fog nodes that communicate with the control center, and the coverage of the control center is divided into several sub-areas. Contains a fog node, each fog node is connected to several electricity meters within its coverage area;

   The control center generates a corresponding set of super-increasing sequences for each electric meter based on the homomorphic Paillier encryption method according to the types of electrical appliances within the coverage area of each electric meter and its energy efficiency level information, and sends the super-increasing sequences to the corresponding electricity meter;

   Each electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, and encrypts the electricity consumption data using the super-increasing sequence to generate ciphertext, and encapsulates the ciphertext into an electricity consumption message and sends it to the corresponding fog node;

   Each fog node aggregates all received ciphertexts to obtain aggregated data, and encapsulates the aggregated data into aggregated messages and sends them to the control center;

   The control center decrypts all the received aggregated data, and obtains electricity consumption data including the types of electrical appliances and their energy efficiency levels within the coverage area of the control center.
2. The fog-based multi-dimensional and multi-angle electricity data aggregation system according to claim 1, characterized in that, before the control center generates the super-increasing sequence, each electricity meter and the fog node submit an identity verification message to the control center, The Control Center verifies the authentication message.
3. The fog-based multi-dimensional and multi-angle electricity data aggregation system according to claim 2, wherein each electricity meter and fog node submit an identity verification message to the control center, and the control center verifies the identity verification message. Validate, which includes:

   The electricity meter generates a first random number, and generates a first digital signature according to the first random number, the virtual identity and the first key, and encapsulates the virtual identity, the first digital signature and the first random number into an authentication message and passes through the corresponding fog. The node sends the control center, wherein the virtual identity is allocated by the control center for identification of the electric meter; the first key is the private key allocated by the control center to the electric meter;

   The fog node generates a second random number, generates a second digital signature according to the identity label, the second random number and the first shared key, and encapsulates the identity label, the second digital signature and the second random number into a message and sends it to the control center, where , the identity label is allocated by the control center and used for identification of the fog node; the first shared key is allocated by the control center as a shared key between the corresponding fog node and the control center;

   The control center verifies the identity of the electric meter according to the virtual identity and the first digital signature, and verifies the identity of the fog node according to the second digital signature.
4. The fog-based multi-dimensional and multi-angle electricity consumption data aggregation system according to claim 3, wherein each electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, and uses the ultra The power consumption data is encrypted in an incremental sequence to generate ciphertext, and the ciphertext is encapsulated into a power consumption message and sent to the corresponding fog node, including:

   The electricity meter collects electricity consumption data including the type of electrical appliance and its energy efficiency level, generates a third random number, encrypts the collected electricity consumption data according to the third random number and the super-increasing sequence, and generates a ciphertext;

   The electricity meter generates a third digital signature according to the ciphertext, the second shared key and the current timestamp, wherein the second shared key is allocated by the control center as a shared key between the corresponding electricity meter and the fog node;

   The electricity meter selects a temporary identity, encapsulates the temporary identity, ciphertext, current time stamp and third digital signature into a electricity consumption message and sends it to the corresponding fog node, wherein the fog node assigns a set of temporary identities to the electricity meter each time;

   The fog node verifies the temporary identity, the current timestamp and the third digital signature, and if the verification is successful, the received data is retained.
5. A fog-based aggregation system for multi-dimensional and multi-angle electricity consumption data according to claim 4, characterized in that, after each temporary identity is used by the electricity meter, the temporary identity is deleted, and when all temporary identities are deleted, the corresponding temporary identity is requested. The fog nodes reassign a set of ephemeral identities.
6. The fog-based multi-dimensional and multi-angle electricity data aggregation system according to claim 3, wherein each fog node aggregates all received ciphertexts to obtain aggregated data, and the aggregated The data is encapsulated into aggregated messages and sent to the control center, including:

   The fog node aggregates all received ciphertexts to obtain aggregated data;

   The fog node generates a fourth digital signature according to the identity tag, aggregated data, first shared key and current timestamp;

   The fog node encapsulates the identity tag, aggregated data, current timestamp and fourth digital signature into an aggregated message and sends it to the control center;

   The control center verifies the current time stamp and the fourth digital signature, and if the verification is successful, the received data is retained.
7. The fog-based multi-dimensional and multi-angle electricity data aggregation system according to claim 3, wherein the control center re-assigns a new virtual identity to each electricity meter after each verification of the identity verification message. .

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