CN117992717B - Cholesky decomposition method taking privacy protection into consideration, intelligent household electrical appliance and server terminal - Google Patents
Cholesky decomposition method taking privacy protection into consideration, intelligent household electrical appliance and server terminal Download PDFInfo
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Abstract
The application relates to the technical field of data processing, and discloses a Cholesky decomposition method, intelligent household electrical appliances and a server terminal which are compatible with privacy protection. The method comprises the following steps: converting data acquired by intelligent household appliances into a symmetrical positive definite matrix to obtain an original matrix; performing blinding treatment on the original matrix to obtain a blinded matrix, and distributing the blinded matrix to a plurality of edge servers of a server terminal according to a set distribution strategy so that the plurality of edge servers calculate Cholesky decomposition on the blinded matrix to obtain a target decomposition result; receiving a target decomposition result, and verifying the correctness of the target decomposition result; and processing the target decomposition result into a result matrix under the condition that the target decomposition result passes the correctness verification. The application can improve the efficiency and accuracy of Cholesky decomposition, thereby improving the effect of executing specific functions of the intelligent household electrical appliance. The server terminal can prevent malicious behaviors of the server terminal, and privacy protection of data of the intelligent household electrical appliance is achieved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a Cholesky decomposition method, intelligent household electrical appliance and a server terminal which are compatible with privacy protection.
Background
In the related art, the smart home devices generally need to process collected data to control the smart home devices to perform specific functions. For example, the air conditioner needs to perform a function of adjusting the temperature and humidity of the room in advance by predicting the change of the outside weather through the collected data.
In general, when analyzing and processing data collected by a smart home device, cholesky decomposition is generally required for the data. However, the computing power of the smart home device is generally weak, so the smart home device has low efficiency and accuracy in computing Cholesky decomposition, which may cause the smart home device to deteriorate the effect of performing a specific function.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the application and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
The embodiment of the disclosure provides a Cholesky decomposition method, intelligent household electrical appliance and a server terminal which give consideration to privacy protection, and can improve the effect of executing specific functions of the intelligent household electrical appliance.
In some embodiments, a Cholesky decomposition method with privacy protection is applied to an intelligent home appliance, and the method comprises the following steps: converting data acquired by intelligent household appliances into a symmetrical positive definite matrix to obtain an original matrix; performing blinding treatment on the original matrix to obtain a blinded matrix, and distributing the blinded matrix to a plurality of edge servers of a server terminal according to a set distribution strategy so that the plurality of edge servers calculate Cholesky decomposition on the blinded matrix to obtain a target decomposition result; receiving a target decomposition result, and verifying the correctness of the target decomposition result; and processing the target decomposition result into a result matrix under the condition that the target decomposition result passes the correctness verification.
Optionally, performing blinding processing on the original matrix to obtain a blinded matrix, including: generating a key matrix based on the random integer; and processing the original matrix based on the key matrix and the transposed encryption of the key matrix to obtain a blinded matrix.
Optionally, allocating the blinding matrix to a plurality of edge servers according to a set allocation policy, including: dividing the blinding matrix into a plurality of submatrices according to rows, wherein the number of the submatrices is the same as that of the edge servers; a plurality of sub-matrices are assigned to a plurality of edge servers.
Optionally, allocating the blinding matrix to a plurality of edge servers according to a set allocation policy, including: dividing a blinding matrix into a plurality of submatrices according to rows; acquiring the computing capacity of each edge server; and distributing a corresponding number of sub-matrixes to the edge server according to the intensity of the operation capability of the edge server.
Optionally, verifying the correctness of the target decomposition result includes: generating a column vector based on the random constant; calculating a target decomposition result, a product of a transpose of the target decomposition result and a column vector as a first check value, and calculating a product of a blinding matrix and the column vector as a second check value; and under the condition that the first check value and the second check value are equal, confirming that the target decomposition result passes the correctness verification.
In some embodiments, a Cholesky decomposition method with privacy protection is applied to a server terminal, wherein the server terminal comprises a plurality of edge servers, and the plurality of edge servers are connected in sequence and in parallel, and the method comprises the following steps: each edge server receives a partial blinding matrix distributed by the intelligent household electrical appliance according to a set distribution strategy; sequentially performing Cholesky decomposition on each element in the allocated partially-blinded matrix by the front edge server to obtain a sub-decomposition result; the sequentially preceding edge server sends the sub-decomposition results to the sequentially following edge server so that the sequentially following edge server performs Cholesky decomposition for each element in the assigned partially blinded matrix in combination with the sub-decomposition results; and sequentially sending the obtained sub-decomposition result to the intelligent household appliance as a target decomposition result at the last edge server.
Optionally, performing Cholesky decomposition for each element in the assigned partially blinded matrix, comprising: calculating the target element according to a first calculation rule under the condition that the currently calculated target element is a diagonal element; and calculating the target element according to a second calculation rule when the currently calculated target element is a non-diagonal element.
Optionally, the first calculation rule includes:
；
wherein, For the kth row and kth column of target elements,Is the result of the calculation of the target element of the kth row and kth column.
Optionally, the second calculation rule includes:
；
wherein, For the target element of the kth row and jth column,For the calculation result of the target element of the kth row and the ith column,For the calculation result of the target element of the j-th row and i-th column,Is the result of calculation for the target element of the j-th row and j-th column.
In some embodiments, a smart home device includes a processor and a memory storing program instructions configured to perform a Cholesky decomposition method as described above that compromise privacy when the program instructions are executed.
In some embodiments, a server terminal includes: the edge servers are connected in sequence and in parallel; a processor and a memory storing program instructions, the processor being configured to perform a Cholesky decomposition method as described above that gives a compromise to privacy when the program instructions are executed.
The Cholesky decomposition method, the intelligent household electrical appliance and the server terminal for privacy protection provided by the embodiment of the disclosure can realize the following technical effects:
In the embodiment of the disclosure, after the intelligent home appliance collects the data, the data is converted into the original matrix in a symmetrical positive format. After the original matrix is subjected to blinding processing, the intelligent household appliance sends the blinding matrix to a plurality of edge servers of the server terminal according to a set allocation strategy, and the plurality of edge servers calculate Cholesky decomposition on the blinding matrix. And after the Cholesky decomposition is completed, the edge servers of the server terminal return the target decomposition result to the intelligent household appliance. Therefore, in the embodiment of the disclosure, the Cholesky decomposition is performed by the server terminal with relatively high computing capability. Therefore, the efficiency and the accuracy of Cholesky decomposition are improved, and the effect of executing specific functions by the intelligent household electrical appliance is further improved.
In the embodiment of the disclosure, the intelligent home appliance performs blinding processing on the original matrix before transmitting the original matrix to a plurality of edge servers of the server terminal. After receiving the target decomposition result sent by the server terminal, the intelligent household appliance performs correctness verification on the target decomposition result, and after passing the correctness verification, the intelligent household appliance restores the target decomposition result to a result matrix. Thus, malicious behaviors of the server terminal can be prevented, and privacy protection of data of the intelligent household electrical appliance is realized.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which like reference numerals refer to similar elements, and in which:
fig. 1 is a schematic diagram of a smart home system provided in an embodiment of the present disclosure;
FIG. 2 is a schematic diagram I of a Cholesky decomposition method with privacy preservation according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram II of a Cholesky decomposition method with privacy preservation according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram III of a Cholesky decomposition method with privacy preservation according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram IV of a Cholesky decomposition method with privacy preservation according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram five of a Cholesky decomposition method with privacy preservation according to an embodiment of the present disclosure;
Fig. 7 is a schematic diagram of an intelligent home appliance provided in an embodiment of the present disclosure;
Fig. 8 is a schematic diagram of a server terminal provided in an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated.
In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
The term "corresponding" may refer to an association or binding relationship, and the correspondence between a and B refers to an association or binding relationship between a and B.
It should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other.
As shown in fig. 1, the intelligent home system includes an intelligent home device 700 and a server terminal 800, wherein information interaction can be performed between the intelligent home device 700 and the server terminal 800.
Specifically, the smart home device 700 is a home device capable of performing a certain specific function through collected data, including but not limited to an air conditioner, a refrigerator, and a television. The server terminal 800 includes a plurality of edge servers, which are connected in series and in parallel.
Optionally, the smart home device 700 includes a processor. The processor can convert data acquired by the data acquisition device arranged on the intelligent household appliance into an original matrix in a symmetrical positive format. The processor can encrypt the original matrix into a blinding matrix, and distribute the blinding matrix to a plurality of edge servers of the server terminal according to a set distribution strategy so as to calculate Cholesky decomposition on the blinding matrix through the plurality of edge servers and obtain a target decomposition result. The processor can receive the target decomposition result returned by the server terminal, and restore the target decomposition result to a result matrix under the condition that the target decomposition result passes the correctness verification.
In combination with the above-mentioned intelligent home appliance, the embodiment of the present disclosure provides a Cholesky decomposition method for privacy protection, as shown in fig. 2, where the Cholesky decomposition method includes:
S201, the processor converts data acquired by the intelligent household electrical appliance into a symmetrical positive definite matrix to obtain an original matrix.
Specifically, the data collected by the intelligent home appliance may be data collected by a certain data collection device (for example, a temperature sensor, a humidity sensor, etc.) set on the intelligent home appliance in a period of time, or may be data currently collected by a plurality of data collection devices set on the intelligent home appliance, or data collected by a plurality of data collection devices in a period of time.
Specifically, since Cholesky decomposition is calculated on data, the data is required to be a symmetric positive definite matrix. Therefore, the processor needs to convert the data collected by the intelligent home device into a symmetric positive definite matrix.
Specifically, the data collected by the intelligent household electrical appliance can be converted into the symmetric positive definite matrix by directly arranging the data collected by the intelligent household electrical appliance according to the format requirement of the symmetric positive definite matrix, so as to obtain the original matrix.
S202, the processor performs blinding processing on the original matrix to obtain a blinded matrix, and distributes the blinded matrix to a plurality of edge servers of the server terminal according to a set distribution strategy, so that the plurality of edge servers calculate Cholesky decomposition on the blinded matrix to obtain a target decomposition result.
Specifically, a plurality of edge servers that send the original matrix to the server terminal calculate Cholesky decomposition, and there is a risk that the private data is compromised by the edge servers. Therefore, the original matrix needs to be blinded before being transmitted to a plurality of edge servers of the server terminal.
Specifically, in some cases, some of the plurality of edge servers on the server terminal side may have a case where the computing power of the edge servers is also weak. Therefore, if a blinding matrix is assigned to these edge servers to compute Cholesky decomposition, the efficiency and accuracy of Cholesky decomposition may remain low. Thus, the processor needs to assign the blinding matrix to a plurality of edge servers according to a set assignment policy.
In some embodiments, assigning the blinding matrix to the plurality of edge servers according to a set assignment policy includes: dividing the blinding matrix into a plurality of submatrices according to rows, wherein the number of the submatrices is the same as that of the edge servers; a plurality of sub-matrices are assigned to a plurality of edge servers.
Specifically, the computing power is the same for the server terminals of the plurality of edge servers. The blinding matrix may be uniformly divided into a plurality of sub-matrices by rows, the number of sub-matrices being the same as the number of edge servers. Therefore, each edge server can execute the same operation amount, and the situation that the overall efficiency of calculating Cholesky decomposition of a blinding matrix by a server terminal is reduced due to excessive operation amount and overlong operation time of a certain edge server is avoided.
Specifically, a plurality of edge servers of the server terminal are connected in sequence and in parallel, and a sequentially preceding edge server may transmit a partial decomposition result to a sequentially following edge server after calculating Cholesky decomposition for a partial blinding matrix allocated thereto. In this way, the edge servers at the end in sequence may derive an overall result of computing Cholesky decomposition for the blinding matrix based on the partial decomposition results of the other edge servers and the partial blinding matrix assigned thereto.
In some embodiments, assigning the blinding matrix to the plurality of edge servers according to a set assignment policy includes: dividing a blinding matrix into a plurality of submatrices according to rows; acquiring the computing capacity of each edge server; and distributing a corresponding number of sub-matrixes to the edge server according to the intensity of the operation capability of the edge server.
Specifically, the computing power is different for a plurality of edge servers. Before the plurality of sub-matrixes are divided and distributed to the plurality of edge servers, the computing capacity of each edge server can be acquired, and then the corresponding number of sub-matrixes are distributed to the edge servers according to the intensity of the computing capacity of the edge servers. Therefore, the edge server with strong operation capacity can execute more operation capacity, the edge server with weak operation capacity can execute less operation capacity, and the situation that the overall efficiency of calculating Cholesky decomposition on the blinding matrix by the server terminal is reduced due to excessive operation capacity of the edge server with weak operation capacity is avoided.
In some embodiments, there are instances where some of the plurality of edge servers are in an operational state. Thus, assigning the blinding matrix to the plurality of edge servers according to the set assignment policy may further include: dividing a blinding matrix into a plurality of submatrices according to rows; acquiring the operation capability and the working state of each edge server; and distributing a corresponding number of sub-matrixes to the edge servers which are not in the working state according to the intensity of the operation capability of the edge servers.
In particular, when the sub-matrix is allocated to the edge server, the sub-matrix is also prevented from being allocated to the edge server in the operating state with reference to the operating state of each edge server. In this way, the risk of the overall efficiency of calculating the Cholesky decomposition of the blinding matrix by the server terminal being reduced due to the fact that a certain edge server cannot execute the current Cholesky decomposition task for a long time in an operating state is reduced
S203, the processor receives the target decomposition result and verifies the correctness of the target decomposition result.
Specifically, there may be a case where a certain edge server in the server terminal does not calculate Cholesky decomposition according to a preset program. Therefore, after receiving the target decomposition result returned by the server terminal, the correctness of the target decomposition result is verified.
S204, the processor processes the target decomposition result into a result matrix under the condition that the target decomposition result passes the correctness verification.
Specifically, if the target decomposition result passes the correctness verification, it indicates that all edge servers of the server terminal calculate Cholesky decomposition on the blinding matrix according to a preset program. In this case, it may be determined that the target decomposition result is authentic. Therefore, when the target decomposition result passes the correctness verification, the target decomposition result can be processed into a result matrix for the reference parameters of the subsequent intelligent household appliance to execute the specific function.
Specifically, the target decomposition result is processed into a result matrix according to the following expression: l=p -1 -L';
Wherein L is a result matrix, P is a key matrix, and L' is a target decomposition result.
Specifically, the target decomposition result is restored by the above-described calculation method using a matrix-by-vector instead of a matrix-by-matrix calculation method. Therefore, the resources and time consumed in the operation process of the intelligent household appliance are effectively reduced.
In the embodiment of the disclosure, after the intelligent home appliance collects the data, the data is converted into the original matrix in a symmetrical positive format. After the original matrix is subjected to blinding processing, the intelligent household appliance sends the blinding matrix to a plurality of edge servers of the server terminal according to a set allocation strategy, and the plurality of edge servers calculate Cholesky decomposition on the blinding matrix. And after the Cholesky decomposition is completed, the edge servers of the server terminal return the target decomposition result to the intelligent household appliance. Therefore, in the embodiment of the disclosure, the Cholesky decomposition is performed by the server terminal with relatively high computing capability. Therefore, the efficiency and the accuracy of Cholesky decomposition are improved, and the effect of executing specific functions by the intelligent household electrical appliance is further improved.
In the embodiment of the disclosure, the intelligent home appliance performs blinding processing on the original matrix before transmitting the original matrix to a plurality of edge servers of the server terminal. After receiving the target decomposition result sent by the server terminal, the intelligent household appliance performs correctness verification on the target decomposition result, and after passing the correctness verification, the intelligent household appliance restores the target decomposition result to a result matrix. Thus, malicious behaviors of the server terminal can be prevented, and privacy protection of data of the intelligent household electrical appliance is realized.
In the embodiment of the disclosure, the server terminal side is provided with a plurality of edge servers, and the edge servers are only responsible for Cholesky decomposition of the partially-blinded matrix, so that compared with a point-to-point calculation mode of transmitting data to the cloud server, the efficiency of calculating Cholesky decomposition can be improved.
The embodiment of the disclosure provides another Cholesky decomposition method taking privacy protection into consideration, as shown in fig. 3, the Cholesky decomposition method includes:
S301, the processor converts data acquired by the intelligent household electrical appliance into a symmetrical positive definite matrix to obtain an original matrix.
S302, the processor generates a key matrix based on the random integer.
Specifically, a random integer set may be preset, and the processor may randomly select from the random integer set to obtain the random integer λ.
Specifically, a random integer sequence may be generated based on the random integer λ. The processor is according toThe key matrix P can be obtained by performing the calculation by means of the (a). Where i is a number from 2 traversals to n and j is a random integer in interval [1, i-1 ].
S303, the processor processes the original matrix based on the key matrix and the transposed encryption of the key matrix to obtain a blinded matrix, and distributes the blinded matrix to a plurality of edge servers of the server terminal according to a set distribution strategy, so that the plurality of edge servers calculate Cholesky decomposition on the blinded matrix to obtain a target decomposition result.
Specifically, the original matrix is encrypted based on the key matrix and the transpose of the key matrix according to the following expression: a' =pap T;
Wherein A is an original matrix, A' is a blinded matrix, P is a key matrix, and P T is a transpose of the key matrix.
S304, the processor receives the target decomposition result and verifies the correctness of the target decomposition result.
S305, the processor processes the target decomposition result into a result matrix when the target decomposition result passes the correctness verification.
In the embodiment of the disclosure, before the original matrix is sent to a plurality of edge servers of a server terminal, the original matrix is encrypted by a key matrix generated by random integers. Thus, malicious behaviors of the server terminal can be prevented, and privacy protection of data of the intelligent household electrical appliance is realized.
The embodiment of the disclosure provides another Cholesky decomposition method taking privacy protection into consideration, as shown in fig. 4, the Cholesky decomposition method includes:
S401, the processor converts data acquired by the intelligent household electrical appliance into a symmetrical positive definite matrix, and an original matrix is obtained.
S402, the processor performs blinding processing on the original matrix to obtain a blinded matrix, and distributes the blinded matrix to a plurality of edge servers of the server terminal according to a set distribution strategy, so that the plurality of edge servers calculate Cholesky decomposition on the blinded matrix to obtain a target decomposition result.
S403, the processor receives the target decomposition result.
S404, the processor generates a column vector based on the random constants.
Specifically, a random constant set may be preset, and the processor randomly selects from the random constant set to obtain a random constant γ, where the random constant γ is greater than 0.
Specifically, by randomly selecting n constants in the interval (0, 2 γ), a column vector τ of length n can be generated.
S405, the processor calculates a target decomposition result, a product of a transpose of the target decomposition result and a column vector as a first check value, and calculates a product of the blinding matrix and the column vector as a second check value;
Specifically, the first check value is calculated according to the following expression: ；
wherein, As a result of the first check value,As a result of the decomposition of the object,The transpose of the target decomposition result, τ, is the column vector.
Specifically, the second check value is calculated according to the following expression: temp=a' ×τ;
Wherein temp is the second check value, A' is the blinding matrix, and τ is the column vector.
Specifically, when all edge servers of the server terminal perform Cholesky decomposition on the blinding matrix according to a preset program, the product of the obtained target decomposition result and the transpose of the target decomposition result should be equal to the blinding matrix. Therefore, the target decomposition result, the product of the transpose of the target decomposition result and the column vector needs to be calculated as a first check value, and the product of the blinding matrix and the column vector needs to be calculated as a second check value, so as to verify the correctness of the target decomposition result.
And S406, the processor confirms that the target decomposition result passes the correctness verification under the condition that the first check value and the second check value are equal.
Specifically, if the first check value and the second check value are equal, it indicates that the product of the target decomposition result and the transpose of the target decomposition result is equal to the blinding matrix, at this time, it may be determined that all edge servers of the server terminal perform Cholesky decomposition on the blinding matrix according to a preset program. Thus, it can be determined that the target decomposition result passes the correctness verification.
Optionally, in the case that the first check value and the second check value are not equal, it is confirmed that the target decomposition result fails the correctness verification.
Specifically, if the first check value and the second check value are not equal, it is indicated that the product of the target decomposition result and the transpose of the target decomposition result is not equal to the blinding matrix, and at this time, it may be determined that there are edge servers among the plurality of edge servers of the server terminal that do not perform Cholesky decomposition on the blinding matrix according to a preset program. Therefore, it can be determined that the target decomposition result fails the correctness verification.
S407, the processor processes the target decomposition result into a result matrix under the condition that the target decomposition result passes the correctness verification.
In the embodiment of the disclosure, after receiving the target decomposition result sent by the server terminal, the correctness of the target decomposition result is verified by calculating the first check value and the second check value, and the target decomposition result is restored to the result matrix after passing the correctness verification. Thus, malicious behaviors of the server terminal can be prevented, and privacy protection of data of the intelligent household electrical appliance is realized.
The server terminal provided by the embodiment of the disclosure comprises a plurality of edge servers, wherein the plurality of edge servers are connected in sequence and in parallel. Each edge server can receive the partially blinded matrix distributed by the intelligent household appliance according to the set distribution strategy. The sequentially preceding edge server may perform Cholesky decomposition for each element in the assigned partially blind matrix, obtaining sub-decomposition results. The sequentially preceding edge server may send the sub-decomposition results to the sequentially following edge server so that the sequentially following edge server performs Cholesky decomposition for each element in the assigned partially blinded matrix in combination with the sub-decomposition results. And sequentially sending the obtained sub-decomposition result to the intelligent household appliance as a target decomposition result at the last edge server.
In combination with the above server terminal, the embodiment of the present disclosure provides a Cholesky decomposition method that gives consideration to privacy protection, as shown in fig. 5, where the Cholesky decomposition method includes:
s501, each edge server receives a partially-blinded matrix distributed by the intelligent household appliance according to a set distribution strategy.
Specifically, the server terminal is provided with a communication interface, and the communication interface is connected with the intelligent household electrical appliance and each edge server. Therefore, each edge server can receive the partially-blinded matrix distributed by the intelligent household appliance according to the set distribution strategy.
S502, the edge server which is arranged in sequence and before performs Cholesky decomposition on each element in the distributed partial blinding matrix to obtain a sub-decomposition result.
Specifically, each edge server has built therein a program that calculates Cholesky decomposition for the matrix. Therefore, upon receiving the partially-blinded matrix, the sequentially preceding edge server performs Cholesky decomposition for each element in the allocated partially-blinded matrix, obtaining a sub-decomposition result.
In some embodiments, performing Cholesky decomposition for each element in the assigned partially blind matrix includes: calculating the target element according to a first calculation rule under the condition that the currently calculated target element is a diagonal element; and calculating the target element according to a second calculation rule when the currently calculated target element is a non-diagonal element.
Specifically, a diagonal element is an element in a matrix at a diagonal position. Since the way Cholesky decomposition is calculated for diagonal and non-diagonal elements in the matrix is different. Therefore, it is necessary to determine whether the currently calculated target element is a diagonal element.
Specifically, in the case where the currently calculated target element is a diagonal element, the target element needs to be calculated in accordance with the first calculation rule.
Optionally, the first calculation rule includes:
；
wherein, For the kth row and kth column of target elements,Is the result of the calculation of the target element of the kth row and kth column.
Note that, since the blinding matrix is obtained by blinding the original matrix of the symmetric positive format, the sub-decomposition result obtained by performing Cholesky decomposition on the diagonal elementsIt is impossible that there is less than 0. Thus, if it occursIf the number is smaller than 0, error information is sent to other edge servers and intelligent household appliances, and the calculation of Cholesky decomposition on the blinding matrix is stopped.
Specifically, in the case where the currently calculated target element is a non-diagonal element, the target element needs to be calculated in accordance with the second calculation rule.
Optionally, the second calculation rule includes:
；
wherein, For the target element of the kth row and jth column,For the calculation result of the target element of the kth row and the ith column,For the calculation result of the target element of the j-th row and i-th column,Is the result of calculation for the target element of the j-th row and j-th column.
S503, the sequentially preceding edge server transmits the sub-decomposition result to the sequentially following edge server, so that the sequentially following edge server performs Cholesky decomposition for each element in the allocated partially blinded matrix in combination with the sub-decomposition result.
Specifically, since the sequentially following edge servers need to incorporate or reference the sub-decomposition results of the preceding edge servers in calculating Cholesky decomposition for each element in the assigned partially blind matrix. Therefore, after the sequentially preceding edge server completes Cholesky decomposition, the sub-decomposition result needs to be sent to the sequentially following edge server.
And S504, sequentially sending the obtained sub-decomposition result to the intelligent household appliance at the last edge server as a target decomposition result.
Specifically, since a plurality of edge servers in the server terminal are sequentially and parallelly connected, each edge server calculates Cholesky decomposition only for a partially blinded matrix allocated thereto, and after the sequentially preceding edge server completes Cholesky decomposition, the resulting sub-decomposition result is transmitted to the sequentially following edge server. Therefore, all the sub-decomposition results of the edge servers that precede in sequence are aggregated at the last edge server in sequence. Thus, the sub-decomposition results sequentially obtained at the last edge server can be taken as the target decomposition results for the blinding matrix.
In the embodiment of the disclosure, the server terminal may receive the blinding matrix sent by the intelligent home appliance according to the set allocation policy, and the plurality of edge servers of the server terminal calculate Cholesky decomposition on the partial blinding matrix respectively. And after the Cholesky decomposition is completed, the edge servers of the server terminal return the target decomposition result to the intelligent household appliance. Therefore, the Cholesky decomposition is calculated without the intelligent household electrical equipment with weak calculation capability, the efficiency and the accuracy of the Cholesky decomposition are improved, and the effect of executing the specific function of the intelligent household electrical equipment is further improved.
In conjunction with the intelligent home appliance system shown in fig. 1, another Cholesky decomposition method for privacy protection is provided in the embodiment of the present disclosure, as shown in fig. 6, where the Cholesky decomposition method includes:
s601, the intelligent household electrical appliance converts the acquired data into a symmetrical positive definite matrix to obtain an original matrix.
S602, the intelligent household appliance performs blinding treatment on the original matrix to obtain a blinded matrix.
And S603, the intelligent household appliance distributes the blinding matrix to a plurality of edge servers of the server terminal according to a set distribution strategy.
And S604, each edge server of the server terminal receives the partially-blinded matrix distributed by the intelligent household appliance according to the set distribution strategy.
S605, the edge server sequentially preceding in the server terminal performs Cholesky decomposition for each element in the allocated partially blinded matrix, obtaining a sub-decomposition result.
S606, the sequentially preceding edge server in the server terminal transmits the sub-decomposition result to the sequentially following edge server, so that the sequentially following edge server performs Cholesky decomposition for each element in the allocated partially blind matrix in combination with the sub-decomposition result.
And S607, sequentially sending the obtained sub-decomposition results to the intelligent household appliance as target decomposition results at the last edge server in the server terminal.
S608, the intelligent household appliance receives the target decomposition result and verifies the correctness of the target decomposition result.
S609, the intelligent household appliance processes the target decomposition result into a result matrix under the condition that the target decomposition result passes the correctness verification.
In the embodiment of the disclosure, after the intelligent home appliance collects the data, the data is converted into the original matrix in a symmetrical positive format. After the original matrix is subjected to blinding processing, the intelligent household appliance sends the blinding matrix to a plurality of edge servers of the server terminal according to a set allocation strategy, and the plurality of edge servers calculate Cholesky decomposition on the blinding matrix. And after the Cholesky decomposition is completed, the edge servers of the server terminal return the target decomposition result to the intelligent household appliance. Therefore, in the embodiment of the disclosure, the Cholesky decomposition is performed by the server terminal with relatively high computing capability. Therefore, the efficiency and the accuracy of Cholesky decomposition are improved, and the effect of executing specific functions by the intelligent household electrical appliance is further improved.
In the embodiment of the disclosure, the intelligent home appliance performs blinding processing on the original matrix before transmitting the original matrix to a plurality of edge servers of the server terminal. After receiving the target decomposition result sent by the server terminal, the intelligent household appliance performs correctness verification on the target decomposition result, and after passing the correctness verification, the intelligent household appliance restores the target decomposition result to a result matrix. Thus, malicious behaviors of the server terminal can be prevented, and privacy protection of data of the intelligent household electrical appliance is realized.
As shown in conjunction with fig. 7, an embodiment of the present disclosure provides an intelligent home device 700, where the intelligent home device 700 includes: a processor (processor) 701 and a memory (memory) 702. Optionally, the apparatus may also include a communication interface (Communication Interface) 703 and a bus 704. The processor 701, the communication interface 703 and the memory 702 may communicate with each other via the bus 704. The communication interface 703 may be used for information transfer. The processor 701 may call logic instructions in the memory 702 to perform the Cholesky decomposition method of the above embodiment that compromise privacy protection.
Further, the logic instructions in the memory 702 described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product.
The memory 702 is used as a computer readable storage medium for storing a software program, a computer executable program, and program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 701 executes the program instructions/modules stored in the memory 702 to perform the functional applications and data processing, i.e., to implement the control method for the air conditioning group control system in the above-described embodiment.
Memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal device, etc. In addition, memory 702 may include high-speed random access memory, and may also include non-volatile memory.
As shown in conjunction with fig. 8, an embodiment of the present disclosure provides a server terminal 800, including: the plurality of edge servers 801 are connected in series and in parallel. The plurality of edge servers 801 are configured to execute a Cholesky decomposition method applied to the server terminal that gives consideration to privacy protection.
Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions configured to perform the Cholesky decomposition method described above that compromise privacy protection.
Embodiments of the present disclosure may be embodied in a software product stored on a storage medium, including one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of a method according to embodiments of the present disclosure. While the aforementioned storage medium may be a non-transitory storage medium, such as: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this disclosure is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in the present disclosure, the terms "comprises," "comprising," and/or variations thereof, mean that the recited features, integers, steps, operations, elements, and/or components are present, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus that includes the element. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units may be merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (9)
1. A Cholesky decomposition method taking privacy protection into consideration is applied to intelligent household appliances, and is characterized by comprising the following steps:
Converting data acquired by intelligent household appliances into a symmetrical positive definite matrix to obtain an original matrix;
Performing blinding treatment on the original matrix to obtain a blinded matrix, and distributing the blinded matrix to a plurality of edge servers of a server terminal according to a set distribution strategy so that the plurality of edge servers calculate Cholesky decomposition on the blinded matrix to obtain a target decomposition result; the method comprises the steps that a plurality of edge servers of a server terminal are connected in sequence and in parallel, and a target decomposition result is a sub-decomposition result obtained in sequence at the last edge server;
Receiving a target decomposition result, and verifying the correctness of the target decomposition result;
under the condition that the target decomposition result passes the correctness verification, processing the target decomposition result into a result matrix;
Distributing the blinding matrix to a plurality of edge servers of the server terminal according to a set distribution strategy, comprising:
dividing a blinding matrix into a plurality of submatrices according to rows;
Acquiring the operation capability and the working state of each edge server;
And distributing a corresponding number of sub-matrixes to the edge servers which are not in the working state according to the intensity of the operation capability of the edge servers.
2. The Cholesky decomposition method of claim 1, wherein blinding the original matrix to obtain a blinded matrix comprises:
Generating a key matrix based on the random integer;
And processing the original matrix based on the key matrix and the transposed encryption of the key matrix to obtain a blinded matrix.
3. The Cholesky decomposition method of claim 1 or 2, wherein verifying correctness of the target decomposition result comprises:
Generating a column vector based on the random constant;
Calculating a target decomposition result, a product of a transpose of the target decomposition result and a column vector as a first check value, and calculating a product of a blinding matrix and the column vector as a second check value;
and under the condition that the first check value and the second check value are equal, confirming that the target decomposition result passes the correctness verification.
4. The Cholesky decomposition method taking privacy protection into consideration is applied to a server terminal, and is characterized in that the server terminal comprises a plurality of edge servers which are connected in sequence and in parallel, and the method comprises the following steps:
each edge server receives a partial blinding matrix distributed by the intelligent household electrical appliance according to a set distribution strategy;
Sequentially performing Cholesky decomposition on each element in the allocated partially-blinded matrix by the front edge server to obtain a sub-decomposition result;
The sequentially preceding edge server sends the sub-decomposition results to the sequentially following edge server so that the sequentially following edge server performs Cholesky decomposition for each element in the assigned partially blinded matrix in combination with the sub-decomposition results;
Sequentially sending the obtained sub-decomposition result to the intelligent household appliance as a target decomposition result at the last edge server;
The blinding matrix is obtained by carrying out blinding treatment on an original matrix obtained by converting data acquired by the intelligent household appliance into a symmetrical positive definite matrix; the intelligent household appliance distributes and sends a part of blinding matrix to the edge server according to a set distribution strategy, and the blinding matrix is required to be divided into a plurality of sub-matrixes according to rows; acquiring the operation capability and the working state of each edge server; and distributing a corresponding number of sub-matrixes to the edge servers which are not in the working state according to the intensity of the operation capability of the edge servers.
5. The Cholesky decomposition method of claim 4, wherein performing Cholesky decomposition for each element in the assigned partially blind matrix comprises:
Calculating the target element according to a first calculation rule under the condition that the currently calculated target element is a diagonal element;
and calculating the target element according to a second calculation rule when the currently calculated target element is a non-diagonal element.
6. The method of claim 5, wherein the first calculation rule comprises:
；
wherein, For the kth row and kth column of target elements,Is the result of the calculation of the target element of the kth row and kth column.
7. The Cholesky decomposition method of claim 5, wherein the second calculation rule comprises:
；
wherein, Is the kth lineThe target element of the column is selected,To the kth lineThe result of the computation of the target element of the column,To the first pairLine 1The result of the computation of the target element of the column,To the first pairLine 1The calculation result of the target element of the column.
8. A smart home device comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the Cholesky decomposition method of any one of claims 1 to 3, when the program instructions are run.
9. A server terminal, comprising:
The edge servers are connected in sequence and in parallel;
The plurality of edge servers are configured to perform the Cholesky decomposition method of any one of claims 4 to 7, while compromising privacy.
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| CN116318690A (en) * | 2023-05-25 | 2023-06-23 | 山东大学 | Safe outsourcing calculation method and system based on matrix singular value decomposition |
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