CN114221809B - Data aggregation system and method for resisting abnormal data and protecting privacy - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
- H04L63/0227—Filtering policies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/60—Arrangements in telecontrol or telemetry systems for transmitting utility meters data, i.e. transmission of data from the reader of the utility meter
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
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Abstract
The invention provides a data aggregation system and a method for resisting abnormal data and protecting privacy, comprising a system model and a security model; in the system model, the intelligent ammeter SM is mainly responsible for measuring the real-time electricity consumption data of a user and reporting the electricity consumption data to the aggregation center AC safely; the aggregation center AC collects the electricity consumption information uploaded by each intelligent ammeter and aggregates the electricity consumption information; after the aggregation process is finished, the aggregation center AC transmits the aggregation result and the pseudo identity information of the abnormal ammeter to the cloud server together; the cloud server CS is responsible for decrypting the encrypted aggregate result, so as to obtain a real aggregate result, so as to make a reasonable production decision and distribute power. The method mainly comprises the following steps: system initialization, user registration, ammeter encryption data, aggregation center aggregation and abnormal data filtering and cloud server decryption data. The invention filters and tracks the abnormal data reported by the intelligent ammeter, thereby improving the accuracy of the aggregation result.
Description
Technical Field
The invention belongs to the technical field of electric power metering, and particularly relates to a data aggregation system and method capable of resisting abnormal data and protecting privacy.
Background
As we use power resources more and more frequently in daily life, the cloud server needs to consider more factors before making production decisions, such as how to guarantee supply-demand balance when the power consumption changes greatly. As an emerging infrastructure, smart grids add upstream information feedback on the basis of traditional grids. The power supply system has the advantages that the power supply can be ensured to be matched with the requirements of users in a short time, and the power supply system has great significance in reasonably distributing power resources and reducing economic losses. To ensure that the cloud server makes appropriate production decisions, the smart grid measures, aggregates, and analyzes the consumer's electricity usage data through the advanced metering infrastructure.
The measurement and collection of the electricity consumption information of the user can necessarily expose personal information such as living habits, economic conditions and the like of the user to researchers, so that personal privacy of the user is infringed. If the personal information of the user falls into the hands of a malicious attacker, the personal safety of the user, the economic benefit and the like can be greatly threatened. It is important that the smart meter encrypts the user data before reporting it to the aggregation center. Currently, encryption technologies for data aggregation in smart grids are mainly divided into two types: an encryption scheme based on homomorphic encryption and an encryption scheme based on mask values. The encryption schemes can effectively ensure that the privacy security of the user is not violated by malicious attackers.
However, there is a remarkable problem in aggregating user electricity data in that abnormal electricity data caused by power theft or meter failure affects the accuracy of the aggregation result. This may not only harm the personal interests of the user, but may also interfere with the production decisions of the cloud server. To our knowledge, none of the existing schemes take into account the effects of outlier data. In their schemes, the aggregation center can only aggregate all the received electricity data, but cannot determine whether the received data is abnormal, and cannot find the source of the abnormal data.
The current data aggregation scheme using homomorphic encryption mainly includes: the Paillier-based encryption scheme is based on the difficult problem of the composite residual class, and is homomorphic encryption meeting the homomorphism of addition and multiplication; an ElGamal-based encryption scheme, the encryption algorithm being based on the difficulty of discrete logarithm interworks in the finite field; and a lattice-based encryption scheme, which can resist quantum attack and improve the effectiveness of the algorithm.
Although the homomorphic encryption data aggregation scheme can effectively protect the personal privacy of users and aggregate data effectively. However, abnormal values in the report data cannot be filtered, so that the final aggregation result contains abnormal data, the accuracy of the aggregation result is reduced, the reasonable distribution of power resources is affected, and even economic loss is caused.
There is also a data aggregation scheme based on a mask value, which encrypts each original data by assigning it a random value. Finally, after aggregation, the sum of all random numbers is eliminated to obtain the real aggregation data.
Similar to the data aggregation scheme using homomorphic encryption, the data aggregation scheme based on the mask value cannot filter the abnormal value in the report data, so that the final aggregation result contains the abnormal data, the accuracy of the aggregation result is reduced, the reasonable distribution of the power resources is affected, and even economic loss is caused.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a lightweight data aggregation scheme which is resistant to abnormal data and can protect privacy, and the method can filter and track the abnormal data reported by the intelligent ammeter while protecting the privacy safety of users, so that the accuracy of an aggregation result is improved. In addition, the invention can complete the filtering of the abnormal data while aggregating without additional process. In addition, the invention uses lightweight matrix encryption, and is more suitable for intelligent electric meters with limited computing capacity.
The specific technical scheme is as follows:
a data aggregation system capable of resisting abnormal data and preserving privacy comprises a system model and a security model;
the system model comprises a smart meter SM, an aggregation center AC and a cloud server CS.
Smart electric meter SM: the smart meter SM is mainly responsible for measuring the user's real-time electricity consumption data and reporting it securely to the aggregation center AC.
Aggregation center AC: in the smart grid system, the aggregation center collects electricity consumption information uploaded by each smart meter and aggregates the electricity consumption information. The aggregation center can also judge whether the encrypted data is abnormal or not and filter the abnormal data. In addition, the invention can also track the source of the abnormal data, namely record the ammeter reporting the abnormal data.
And after the aggregation process is finished, the aggregation center AC transmits the aggregation result and the pseudo identity information of the abnormal ammeter to the cloud server together.
Cloud server CS: the cloud server CS is responsible for decrypting the encrypted aggregate result, so as to obtain a real aggregate result, so as to make a reasonable production decision and distribute power. And, the cloud server CS can check and repair the abnormal ammeter according to its pseudo identity information.
Under the system model and the security model, the invention provides a lightweight data aggregation scheme which is resistant to abnormal data and protects privacy. In particular, the following three objectives should be achieved:
lightweight class: the lightweight matrix encryption is used, and is different from other time-consuming computing operations, and is more suitable for intelligent electric meters with limited computing capacity.
Anti-anomaly data and privacy preserving: on the premise of protecting the privacy safety of users, abnormal electricity utilization data is filtered, and normal electricity utilization data is aggregated, so that an accurate aggregation result is obtained.
Efficiency is that: the proposed scheme should be effective. To achieve a practical data aggregation scheme, both security and efficiency issues should be considered to find a trade-off solution.
The invention provides a data aggregation method for resisting abnormal data and protecting privacy, which mainly comprises the following steps: system initialization, user registration, ammeter encryption data, aggregation center aggregation and abnormal data filtering and cloud server decryption data.
Step 1: system initialization
Cloud server CS generates two random nonsingular matricesAnd calculate their inverse matrix +.>The common parameter of the system can be expressed as +.>
Step 2: user registration
When smart electric meter SM i When registering with the cloud server CS, the cloud server CS generates a random number r for the cloud server CS i And a pseudo-identity information PID i . Then, the cloud server CS handles { PID over a secure channel i ,r i And the information is sent to the smart meter.
Step 3: smart electric meter SM i Encrypting electricity data x i
First according to the electricity consumption data x i Construction of matrixAnd correspondingly encrypt it to generate { HT i,1 ,HT i,2 }. Then will ciphertext { HT i,1 ,HT i,2 And transmitted to the aggregation center AC.
Step 4: polymerization center AC performs polymerization and filtration
Aggregation center AC generates matrix according to critical value q of normal dataAnd generates TT by encrypting the data correspondingly. Use smart meter SM i Reporting data { HT of (C) i,1 ,HT i,2 Data aggregation with generated TT operation, automaticallyFiltering out abnormal data to obtain an aggregation result R'. And, can be according to formula HT i,1 TTHT i,2 Finding out the source of abnormal data and recording the pseudo-identity information PID of the intelligent ammeter ab . Finally, the aggregation center aggregates the result R' and the pseudo identity information { PID ] of the abnormal ammeter ab And is sent to the cloud server CS.
Step 5: cloud server CS decryption
The cloud server CS receives the aggregation result R' and pseudo-identity information { PID (proportion integration differentiation) of the abnormal ammeter from the aggregation center AC ab And after the data are decrypted, the real aggregation result R and the information of the abnormal ammeter are obtained.
Drawings
FIG. 1 is a schematic diagram of the system architecture of the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
The specific technical scheme of the invention is described by combining the embodiments.
In this embodiment, a system model and a security model are defined;
(1) System model
As shown in fig. 1, the system model of the present invention is mainly composed of the following three entities: smart meter SM, aggregation center AC and cloud server CS.
Smart electric meter SM: in the system model, the smart meter SM is mainly responsible for measuring the user's real-time electricity data and reporting it securely to the aggregation center AC.
Aggregation center AC: in the smart grid system, the aggregation center collects electricity consumption information uploaded by each smart meter and aggregates the electricity consumption information. Compared with the common aggregation process, the aggregation center can also judge whether the encrypted data is abnormal or not and filter the abnormal data. Notably, this function can be performed at the same time as the aggregation without additional procedures. In addition, the invention can also track the source of the abnormal data, namely record the ammeter reporting the abnormal data.
And after the aggregation process is finished, the aggregation center AC transmits the aggregation result and the pseudo identity information of the abnormal ammeter to the cloud server together.
Cloud server CS: the cloud server CS is responsible for decrypting the encrypted aggregate result, so as to obtain a real aggregate result, so as to make a reasonable production decision and distribute power. And, the cloud server CS can check and repair the abnormal ammeter according to its pseudo identity information.
(2) Safety model
The user may not only attempt to steal electricity by destroying the smart meter, but may also be interested in the electricity consumption data of other users, and thus crack it. In addition, an ammeter fault may occur to report abnormal electricity data.
The aggregation center AC and the cloud server CS are semi-honest. This means that the two entities will perform the proposed protocol faithfully and will not tamper with the calculation results, but they may get as much knowledge of the individual's electricity usage data as possible. In addition, the aggregation center and the cloud server are not mutually communicated.
Any probabilistic polynomial time adversary can listen to the smart meter and the aggregation center, and the channel between the aggregation center and the cloud server to intercept the reported data.
Under the system model and the security model, the embodiment provides a lightweight data aggregation scheme which is resistant to abnormal data and protects privacy. A system flow diagram of the present invention is shown in fig. 2.
Step 1: system initialization
Cloud server CS generates two random nonsingular matricesAnd->And calculate their inverse matrix +.>The common parameter of the system can be expressed as +.>
Step 2: user registration
When smart electric meter SM i When registering with the cloud server CS, the cloud server CS generates a random number r for the cloud server CS i And a pseudo-identity information PID i . Then, the cloud server CS handles { PID over a secure channel i ,r i And the information is sent to the smart meter.
Step 3: smart electric meter SM i Encrypting data
I. Smart electric meter SM i Based on detected electricity data x i Construction of matrix
a. According to x i Is selected to satisfy x i ∈[0,N 2 -1]
b. As a value in matrix N, x i With their corresponding row and column coordinatesAnd->Can be calculated according to the following formula.
c. Based onConstruction and x i Correlated n-dimensional vector
Wherein the method comprises the steps ofIs->Zero vector of dimension; />Is->A dimension vector, all its elements being 1; />Is an n-dimensional unit vector, its +.>The number of elements is 1.
d. Constructing a matrix from the n-dimensional vectors in the previous step
Wherein x is i R is the original electricity consumption data of the user i As a mask value for the generated random number.
Wherein R is x,i ＝[μ x,i μ x,i ],And mu x,i And mu' x,i Is a generated random number.
II. Smart electric meter SM i Will beEncrypted into ciphertext { HT i,1 ,HT i,2 }：
III. Smart electric meter SM i Ciphertext { HT i,1 ,HT i,2 And transmitted to the aggregation center AC.
Step four: polymerization center AC polymerization and filtration
I. Aggregation center AC generates matrix according to critical value q of normal data
a. The aggregation center AC generates a 2n× (n+1) dimensional matrix Q from Q. The matrix satisfies
Q[i b ,1]＝Q[N+i b ,j b +1]＝1 (7)
And all other elements are 0.
b. Generating matrix R Q 。
Wherein r is Q,1 ，r Q,2 And r Q,3 Is a generated random number.
c. Generating a matrix
Aggregation center AC matrixEncrypted as TT.
III, the aggregation center AC transmits the intelligent ammeter SM i Reporting data { HT of (C) i,1 ,HT i,2 And performing matrix multiplication operation on the generated TT and the generated TT, thereby obtaining an aggregation result R'.
Wherein the method comprises the steps of
For outlier data, XQX' T And thus the formula HT i,1 TTHT i,2 The result of (2) was 0. Whereas for normal data XQX' T =1, formula HT i,1 TTHT i,2 The result of (1) is (x i +r i ). Thus, the abnormal data can be automatically filtered in the aggregation process, namely, the aggregation result R' is sigma (x) m +r m ) Wherein x is m Representing normal electricity data, r m Representing its corresponding mask value. In addition, if a reported data is determined to be anomalous, the hub AC will record its source PID ab And sends it to the cloud server CS. Iv. the polymerization center will polymerize the result R' = Σ (x m +r m ) Pseudo identity information { PID (proportion integration differentiation) of intelligent ammeter corresponding to abnormal data ab And is sent to the cloud server CS.
Step 5: cloud server CS decryption
The cloud server CS receives an aggregation result R' = Σ (x) from the aggregation center AC m +r m ) And pseudo identity information { PID for abnormal electricity meter ab After } the data is decrypted according to the following equation, and an accurate aggregation result R is obtained.
R＝∑(x m +r m )-∑r m ＝∑(x m +r m )-(∑r i -∑r ab )＝∑x m (12)
Wherein r is ab And the mask value corresponding to the abnormal report data.
Therefore, the cloud server CS may obtain the aggregation result of screening out the abnormal data and the pseudo identity information of the abnormal electric meter, so as to make a reasonable production decision and check the abnormal intelligent electric meter.
Claims (1)
1. A data aggregation method for resisting abnormal data and protecting privacy adopts a data aggregation system for resisting abnormal data and protecting privacy, which comprises a system model and a security model;
the system model comprises a smart meter SM, an aggregation center AC and a cloud server CS;
smart electric meter SM: the intelligent ammeter SM is mainly responsible for measuring the real-time electricity consumption data of the user and reporting the real-time electricity consumption data to the aggregation center AC safely;
aggregation center AC: in the intelligent power grid system, an aggregation center collects power consumption information uploaded by each intelligent electric meter and aggregates the power consumption information; after the aggregation process is finished, the aggregation center AC transmits the aggregation result and the pseudo identity information of the abnormal ammeter to the cloud server together;
cloud server CS: the cloud server CS is responsible for decrypting the encrypted aggregation result, so that a real aggregation result is obtained, and reasonable production decision and power distribution are conveniently carried out; the cloud server CS checks and maintains the abnormal ammeter according to the pseudo identity information of the abnormal ammeter;
the polymerization center AC: the method is also used for judging whether the encrypted data is abnormal or not and filtering the abnormal data; the system is also used for tracking the source of the abnormal data, namely recording the ammeter reporting the abnormal data;
the method is characterized by comprising the following steps:
step 1: system initialization
Cloud server CS generates two random nonsingular matricesAnd calculate their inverse matrix +.>The common parameter of the system can be expressed as +.>
Step 2: user registration
When smart electric meter SM i When registering with the cloud server CS, the cloud server CS generates a random number r for the cloud server CS i And a pseudo-identity information PID i The method comprises the steps of carrying out a first treatment on the surface of the Then, the cloud server CS handles { PID over a secure channel i ,r i Transmitting to the smart meter;
step 3: smart electric meter SM i Encrypting electricity data x i
I. Smart electric meter SM i Based on detected electricity data x i Construction of matrix
a. According to x i Is selected to satisfy x i ∈[0,N 2 -1]
b. As a value in matrix N, x i With their corresponding row and column coordinatesAnd->Calculated according to the following formula:
c. based onConstruction and x i Correlated n-dimensional vector:
wherein the method comprises the steps ofIs->Zero vector of dimension; />Is->A dimension vector, all its elements being 1; />Is an n-dimensional unit vector, its +.>The number of the elements is 1;
d. constructing a matrix from the n-dimensional vectors in the previous step
Wherein x is i R is the original electricity consumption data of the user i As a mask value for the generated random number;
wherein R is x,i ＝[μ x,i μ x,i ],And mu x,i And mu' x,i Is a generated random number;
II. Smart electric meter SM i Will beEncrypted into ciphertext { HT i,1 ,HT i,2 }：
III. Smart electric meter SM i Ciphertext { HT i,1 ,HT i,2 Transmitting to the aggregation center AC;
step 4: polymerization center AC performs polymerization and filtration
I. Aggregation center AC generates matrix according to critical value q of normal data
a. The aggregation center AC generates a 2N x (N+1) dimensional matrix Q according to Q; the matrix satisfies:
Q[i b ,1]＝Q[N+i b ,j b +1]＝1 (7)
and all other elements are 0;
b. generating matrix R Q ；
Wherein r is Q,1 ，r Q,2 And r Q,3 Is a generated random number;
c. generating a matrix
Aggregation center AC matrixEncrypting into TT;
III, the aggregation center AC transmits the intelligent ammeter SM i Reporting data { HT of (C) i,1 ,HT i,2 Performing matrix multiplication operation on the generated TT and the generated TT to obtain an aggregation result R';
wherein the method comprises the steps of
For outlier data, XQX' T And thus the formula HT i,1 TTHT i,2 The result of (2) is 0; whereas for normal data XQX' T =1, formula HT i,1 TTHT i,2 The result of (1) is (x i +r i ) The method comprises the steps of carrying out a first treatment on the surface of the Thus, the abnormal data can be automatically filtered in the aggregation process, namely, the aggregation result R' is sigma (x) m +r m ) Wherein x is m Representing the data of the electricity being used in general,r m representing its corresponding mask value; in addition, if a reported data is determined to be anomalous, the hub AC will record its source PID ab And sends it to the cloud server CS;
iv. the polymerization center will polymerize the result R' = Σ (x m +r m ) Pseudo identity information { PID (proportion integration differentiation) of intelligent ammeter corresponding to abnormal data ab Transmitting to the cloud server CS;
step 5: cloud server CS decryption
The cloud server CS receives the aggregation result R' and pseudo-identity information { PID (proportion integration differentiation) of the abnormal ammeter from the aggregation center AC ab And after the data are decrypted, the real aggregation result R and the information of the abnormal ammeter are obtained.
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