CN114221809A - Abnormal data resisting and privacy protecting data aggregation system and method - Google Patents
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- H04L63/00—Network architectures or network communication protocols for network security
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Abstract
The invention provides a data aggregation system and a method for resisting abnormal data and protecting privacy, wherein the data aggregation system comprises a system model and a safety model; in the system model, the intelligent electric meter SM is mainly responsible for measuring the real-time electricity consumption data of a user and safely reporting the data to the aggregation center AC; the aggregation center AC collects the electricity utilization information uploaded by each intelligent electricity meter and aggregates the electricity utilization information; after the aggregation process is finished, the aggregation center AC sends the aggregation result and the pseudo-identity information of the abnormal electric meter to the cloud server together; the cloud server CS is responsible for decrypting the encrypted aggregated result to obtain a real aggregated result, so as to make a reasonable production decision and power distribution. The method mainly comprises the following steps: the method comprises the steps of system initialization, user registration, ammeter encryption data, aggregation and abnormal data filtering of an aggregation center and cloud server decryption data. According to the method and the system, the abnormal data reported by the intelligent electric meter are filtered and tracked, so that the accuracy of the aggregation result is improved.
Description
Technical Field
The invention belongs to the technical field of electric power metering, and particularly relates to a data aggregation system and method for resisting abnormal data and protecting privacy.
Background
As power resources are used more and more frequently in daily life, the cloud server needs to consider more factors before making a production decision, such as how to ensure balance between supply and demand when power consumption changes greatly. As an emerging infrastructure, the smart grid is added with uplink information feedback on the basis of the traditional power grid. Its advantage is that it can ensure the supply of electric power to match the demand of user in short time, which has great significance for reasonably distributing electric power resource and reducing economic loss. To ensure that the cloud servers make appropriate production decisions, the smart grid measures, aggregates, and analyzes the customer's power usage data through an advanced metering infrastructure.
The measurement and collection of the electricity consumption information of the user can lead personal information of life habits, economic conditions and the like of the user to be exposed to researchers, and therefore the personal privacy of the user is invaded. If the personal information of the user falls into the hands of a malicious attacker, the personal safety, economic benefits and the like of the user are greatly threatened. It is important that the smart meter encrypts the user data before it is reported to the aggregation center. At present, encryption technologies for data aggregation in smart grid are mainly divided into two types: a homomorphic encryption based encryption scheme and a masked value based encryption scheme. The encryption schemes can effectively ensure that the privacy security of the user is not invaded by a malicious attacker.
However, there is a significant problem in aggregating customer electricity data that abnormal electricity data caused by power theft or meter malfunction affects the accuracy of the aggregated result. This not only harms the user's personal interests, but also interferes with the cloud server's production decisions. To our knowledge, none of the existing solutions takes into account the effects of anomalous data. In their solutions, the aggregation center can only aggregate all the received power consumption data, but cannot determine whether the received data is abnormal, and even cannot find the source of the abnormal data.
The current data aggregation scheme using homomorphic encryption mainly includes: the encryption scheme based on Paillier, the encryption algorithm is based on the difficult problem of compound residue class, and is homomorphic encryption which meets the addition and multiplication homomorphism; an encryption scheme based on ElGamal, wherein the encryption algorithm is based on the difficulty of discrete logarithm problem in a finite field; and a lattice-based encryption scheme, which can resist quantum attacks and improve the effectiveness of the algorithm.
Although the data aggregation scheme using homomorphic encryption can effectively protect the personal privacy of the user and effectively aggregate data. But 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 influenced, and even economic loss is caused.
There is also a masked value based data aggregation scheme that encrypts each original data by assigning it a random value. Finally, after aggregation, the sum of all random numbers is eliminated to obtain real aggregated data.
Similar to the data aggregation scheme using homomorphic encryption, the data aggregation scheme based on the masked value cannot filter abnormal values in the report data, which may result in that the final aggregation result includes abnormal data, reduce the accuracy of the aggregation result, affect the reasonable allocation of power resources, and even cause economic loss.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a lightweight data aggregation scheme that is resistant to abnormal data and protects privacy, and the abnormal data reported by a smart meter is filtered and tracked while protecting the privacy security of a user, so as to improve the accuracy of an aggregation result. In addition, the invention can complete the filtering of abnormal data while aggregating without additional process. In addition, the method uses lightweight matrix encryption, and is more suitable for the intelligent electric meter with limited computing capacity.
The specific technical scheme is as follows:
a data aggregation system which is resistant to abnormal data and protects 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.
The intelligent 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.
Polymerization center AC: in the smart grid system, a gathering center collects the electricity utilization information uploaded by each smart meter and gathers the electricity utilization 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 abnormal data, namely, records the electric meter reporting the abnormal data.
And after the aggregation process is finished, the aggregation center AC sends the aggregation result and the pseudo-identity information of the abnormal electric meter to the cloud server together.
Cloud server CS: the cloud server CS is responsible for decrypting the encrypted aggregated result to obtain a real aggregated result, so as to make a reasonable production decision and power distribution. And the cloud server CS can check and maintain the abnormal electric meter according to the false identity information of the abnormal electric meter.
Under the system model and the security model, the invention provides a lightweight data aggregation scheme which is resistant to abnormal data and capable of protecting privacy. In particular, the following three objectives should be achieved:
light weight: the method is different from other time-consuming computing operations by using lightweight matrix encryption, and is more suitable for the smart meter with limited computing capacity.
Anti-abnormal data and privacy protection: on the premise of protecting the privacy and safety of the user, abnormal electricity utilization data are filtered, and normal electricity utilization data are aggregated, so that an accurate aggregation result is obtained.
Efficiency: the proposed solution should be efficient. To implement 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: the method comprises the steps of system initialization, user registration, ammeter encryption data, aggregation and abnormal data filtering of an aggregation center and cloud server decryption data.
Step 1: system initialization
Cloud server CS generates two random nonsingular matrices
And calculates their inverse matrices
The common parameters of the system can be expressed as
Step 2: user registration
When the intelligent electric meter SMiWhen registering with the cloud server CS, the cloud server CS generates a random number r for itiAnd a pseudo-identity information PIDi. The cloud server CS then sends the { PID over a secure channeli，riSending the data to the intelligent ammeter.
And step 3: smart electric meter SMiEncrypting electricity data xi
Firstly according to the electricity utilization data xiConstructing matrices
And correspondingly encrypted to generate { HTi，1，HTi，2}. Ciphertext { HT)i，1，HTi，2It sends it to the aggregation center AC.
And 4, step 4: polymerization center AC polymerization and filtration
The aggregation center AC generates a matrix according to a critical value q of the normal data
And encrypting the data to generate TT correspondingly. Using smart meters SMiReport data of { HTi，1，HTi，2And aggregating data with the generated TT operation, automatically filtering abnormal data, and further obtaining an aggregation result R'. And, can be according to formula HTi，1 TTHTi，2Finding the source of the abnormal data and recording the pseudo identity information PID of the intelligent electric meterab. Finally, the aggregation center combines the aggregation result R' and the pseudo identity information { PID (proportion integration differentiation) of the abnormal number electric meterabAnd sending the data to the cloud server CS.
And 5: cloud server CS decryption
The cloud server CS receives the aggregation result R' and the pseudo identity information { PID (proportion integration differentiation) of the abnormal electric meter from the aggregation center ACabAnd 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 embodiment.
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: the system comprises a smart meter SM, an aggregation center AC and a cloud server CS.
The intelligent electric meter SM: in the present system model, the smart meters SM are mainly responsible for measuring the real-time electricity consumption data of the users and reporting them safely to the aggregation center AC.
Polymerization center AC: in the smart grid system, a gathering center collects the electricity utilization information uploaded by each smart meter and gathers the electricity utilization 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. It is noted that this function can be performed at the same time as the aggregation, without additional processes. In addition, the invention can also track the source of abnormal data, namely, records the electric meter reporting the abnormal data.
And after the aggregation process is finished, the aggregation center AC sends the aggregation result and the pseudo-identity information of the abnormal electric meter to the cloud server together.
Cloud server CS: the cloud server CS is responsible for decrypting the encrypted aggregated result to obtain a real aggregated result, so as to make a reasonable production decision and power distribution. And the cloud server CS can check and maintain the abnormal electric meter according to the false identity information of the abnormal electric meter.
(2) Safety model
The user not only can try to steal electricity by destroying the intelligent electric meter, but also can be interested in electricity consumption data of other users and further crack the electricity consumption data. In addition, a fault in the electricity meter may occur to report abnormal electricity consumption 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 will probably know as much as possible about the electricity usage data of the individual. In addition, the aggregation center and the cloud server are not communicated with each other.
Any probabilistic polynomial time adversary can listen to the smart meter and the aggregation center, as well as 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 for resisting abnormal data and protecting privacy. The system flow diagram of the present invention is shown in fig. 2.
Step 1: system initialization
Cloud server CS generates two random nonsingular matrices
And
and calculates their inverse matrices
Of a systemThe common parameter can be expressed as
Step 2: user registration
When the intelligent electric meter SMiWhen registering with the cloud server CS, the cloud server CS generates a random number r for itiAnd a pseudo-identity information PIDi. The cloud server CS then sends the { PID over a secure channeli，riSending the data to the intelligent ammeter.
And step 3: smart electric meter SMiEncrypting data
I. Smart electric meter SMiAccording to the detected electricity data xiConstructing matrices
a. According to xiIs selected to satisfy xi∈[0，N2-1]
b. As a value in the matrix N, xiWith their corresponding row-column coordinates
And is
Can be calculated according to the following formula.
c. Based on
Construction with xiRelated n-dimensional vector
Wherein
Is composed of
A quasi-zero vector;
is composed of
A dimension vector, all its elements being 1;
is an n-dimensional unit vector, its first
Each element is 1.
d. Constructing a matrix according to the n-dimensional vectors in the last step
Wherein xiAs raw electricity consumption data of the user, riThe generated random number is used as its mask value.
Wherein R isx，i＝[μx，i μx，i]，
And μx，iAnd mu'x，iIs a generated random number.
Smart meter SMiWill be provided with
Encrypted into ciphertext { HTi，1，HTi，2}：
Smart meter SMiCipher text { HTi，1，HTi，2It sends it to the aggregation center AC.
Step four: polymerization center AC polymerization and filtration
I. The aggregation center AC generates a matrix according to a critical value q of the normal data
a. The aggregation center AC generates a 2N × (N +1) -dimensional matrix Q from Q. The matrix satisfies
Q[ib，1]＝Q[N+ib，jb+1]＝1 (7)
And all other elements are 0.
b. Generating a matrix RQ。
Wherein r isQ，1，rQ，2And rQ，3Is a generated random number.
c. Generating a matrix
Aggregation center AC matrix
Encrypted to TT.
Aggregation center AC with Smart meters SMiReport data of { HTi，1，HTi，2And multiplying the generated TT by the matrix as follows to obtain an aggregation result R'.
Wherein
For abnormal data, XQX'TResult of (1) is 0, thus formula HTi，1 TTHTi，2The result of (2) is 0. And for normal data, XQX'T1, formula HTi，1 TTHTi，2The result of (a) is (x)i+ri). Thus, abnormal data can be automatically filtered in the aggregation process, namely the aggregation result R' is sigma (x)m+rm) Wherein x ismIndicating normal electricity consumption data, rmIndicating its corresponding mask value. In addition, if a reported data is determined to be anomalous, the aggregation center AC will record its source PIDabAnd sends it to the cloud server CS.
The polymerization center converts the polymerization result R' ═ Σ (x)m+rm) False identity information { PID (proportion integration differentiation) of intelligent electric meter corresponding to abnormal dataabAnd sending the data to the cloud server CS.
And 5: cloud server CS decryption
The cloud server CS receives the aggregation result R' ═ Σ (x) from the aggregation center ACm+rm) And pseudo identity information of abnormal electric meter { PIDabAnd after the data are decrypted according to the following equation, an accurate polymerization result R is obtained.
R＝∑(xm+rm)-∑rm＝∑(xm+rm)-(∑ri-∑rab)＝∑xm (12)
Wherein r isabThe mask value corresponding to the abnormal report data.
Therefore, the cloud server CS can obtain the aggregation result of the filtered abnormal data and the false identity information of the abnormal electric meter, so as to make a reasonable production decision and check the abnormal smart electric meter.
Claims (4)
1. The data aggregation system is characterized by comprising a system model and a security model;
the system model comprises a smart meter SM, an aggregation center AC and a cloud server CS;
the intelligent electric meter SM: the intelligent electric meter SM is mainly responsible for measuring real-time electricity consumption data of a user and safely reporting the data to the aggregation center AC;
polymerization center AC: in the intelligent power grid system, a gathering center collects the power utilization information uploaded by each intelligent electric meter and gathers the power utilization information; after the aggregation process is finished, the aggregation center AC sends the aggregation result and the pseudo-identity information of the abnormal electric meter to the cloud server together;
cloud server CS: the cloud server CS is responsible for decrypting the encrypted aggregation result so as to obtain a real aggregation result, and therefore reasonable production decision and power distribution can be conveniently carried out; and the cloud server CS checks and maintains the abnormal electric meter according to the false identity information of the abnormal electric meter.
2. The system for data aggregation with anti-anomaly and privacy protection according to claim 1, wherein the aggregation center AC: the device is also used for judging whether the encrypted data is abnormal or not and filtering the abnormal data; and also for tracking the source of the abnormal data, i.e. recording the electricity meter reporting the abnormal data.
3. A data aggregation method for resisting abnormal data and protecting privacy, which is based on the system model and the security model of claim 1 or 2, and comprises the following steps: the method comprises the steps of system initialization, user registration, ammeter encryption data, aggregation and abnormal data filtering of an aggregation center and cloud server decryption data.
4. The method for aggregating data according to claim 1, wherein the method comprises the following steps:
step 1: system initialization
Cloud server CS generates two random nonsingular matrices
And calculates their inverse matrices
The common parameters of the system can be expressed as
Step 2: user registration
When the intelligent electric meter SMiWhen registering with the cloud server CS, the cloud server CS generates a random number r for itiAnd a pseudo-identity information PIDi(ii) a The cloud server CS then sends the { PID over a secure channeli，riSending the data to the intelligent ammeter;
and step 3: smart electric meter SMiEncrypting electricity data xi
Firstly according to the electricity utilization data xiConstructing matrices
And correspondingly encrypted to generate { HTi，1，HTi，2}; ciphertext { HT)i，1，HTi，2Sending it to the aggregation center AC；
And 4, step 4: polymerization center AC polymerization and filtration
The aggregation center AC generates a matrix according to a critical value q of the normal data
And carrying out corresponding encryption on the TT to generate TT; using smart meters SMiReport data of { HTi，1，HTi，2Carrying out data aggregation with the generated TT operation, and automatically filtering abnormal data to obtain an aggregation result R'; and, can be according to formula HTi，1TTHTi，2Finding the source of the abnormal data and recording the pseudo identity information PID of the intelligent electric meterab(ii) a Finally, the aggregation center combines the aggregation result R' and the pseudo identity information { PID (proportion integration differentiation) of the abnormal number electric meterabSending the data to a cloud server CS;
and 5: cloud server CS decryption
The cloud server CS receives the aggregation result R' and the pseudo identity information { PID (proportion integration differentiation) of the abnormal electric meter from the aggregation center ACabAnd after the data are decrypted, the real aggregation result R and the information of the abnormal ammeter are obtained.
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| 吴云;吕伟东;李春哲;滕晓红;: "基于身份认证和聚合加密的智能电表数据隐私保护方案", 东北电力大学学报, no. 05 * |
| 张键红;肖晗;王继林;: "高效的基于身份RSA多重数字签名", 小型微型计算机系统, no. 09 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115629783A (en) * | 2022-10-27 | 2023-01-20 | 北方工业大学 | Model updating method for keeping privacy and resisting abnormal data in mobile crowd sensing |
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