A data security protection method based on ownership separation

Abstract

The present invention relates to the field of data security processing technology, and in particular to a data security protection method based on ownership separation, comprising: step S1, setting the upload time, upload method, and upload location of private feature data and non-private feature data; step S2, uploading them to the upload location for storage; step S3, optimizing the settings of the upload time, upload method, and upload location of the private feature data; step S4, adjusting the optimization method for setting the upload time, upload method, and upload location of the private feature data; step S5, deleting the private data of abnormal edge nodes; step S6, optimizing the judgment process of the storage status of the private data of abnormal edge nodes; and step S7, adjusting the upload time, upload method, and upload location of the private data of abnormal edge nodes in step S1. The present invention improves the data security protection efficiency of artificial intelligence robots.

Description

Technical Field

[0001] The present invention relates to the technical field of data security processing, and in particular to a data security protection method based on ownership separation. Background Art

[0002] In the field of AI robotics, due to their close interaction with user privacy, implementing a separation of ownership of user privacy data is crucial. This safeguard ensures users feel safer and more confident when using AI robots to handle delicate tasks in their daily lives and work. However, current AI robots still face numerous challenges in processing user data, struggling to effectively balance computing power with data encryption. Furthermore, while federated learning can help protect privacy, it comes with relatively high time and operational costs. Centralized training, on the other hand, cannot fully guarantee user privacy and data security. Therefore, these issues urgently need to be addressed to promote the further development of AI robotics.

[0003] Chinese patent application publication number CN117376015A discloses a security authentication method for home robots. This method, applied to the field of robot security, addresses the privacy and data security challenges faced by home robots. The robot first completes identity registration on a server. After registration, the robot node's ID, public key, and other related information are stored on the server. The server's ID, public key, and other related information are also stored on the robot node for subsequent identity authentication and key exchange. After mutual authentication, the server and robot node use the disclosed information to establish a session key to ensure communication security. However, this solution struggles to effectively balance the conflict between computing power and data encryption. Summary of the Invention

[0004] To this end, the present invention provides a data security protection method based on separation of ownership to overcome the problems in the prior art of low efficiency in data security protection of artificial intelligence robots due to the high time and operating costs of federated training, the inability of centralized training to ensure user privacy, and the conflicts between device computing power and encryption processing.

[0005] To achieve the above objectives, the present invention provides a data security protection method based on ownership separation, comprising:

[0006] Step S1: Identify the operating data according to the data privacy identification algorithm to obtain private feature data and non-private feature data, and set the upload time, upload method and upload location of the private feature data and non-private feature data;

[0007] Step S2, uploading the private feature data and the non-private feature data to an upload location for storage according to the upload time and upload method of the private feature data and the non-private feature data;

[0008] Step S3: judging the attributes of the private feature data according to the location where the private feature data is generated, obtaining edge node private data and central node private data, and optimizing the upload time, upload method, and upload location of the private feature data according to the attributes of the private feature data;

[0009] Step S4, judging the abnormality of the edge node private data, obtaining the abnormal edge node private data and the normal edge node private data, and adjusting the optimization method for setting the upload time, upload method and upload location of the private feature data;

[0010] Step S5: judging the storage status of the abnormal edge node private data according to the number of calls and storage duration of the abnormal edge node private data, and deleting the abnormal edge node private data according to the storage status of the abnormal edge node private data;

[0011] Step S6: Optimize the process of determining the storage status of private data of abnormal edge nodes based on the storage ratio of adjacent edge nodes;

[0012] Step S7: The importance of the private data of the abnormal edge node is judged according to the secondary call rate of the private data of the abnormal edge node, and the upload time, upload method and upload location of the private data of the abnormal edge node in step S1 are adjusted according to the importance of the private data of the abnormal edge node.

[0013] Furthermore, the data security protection method based on separation of ownership is applied to the data processing system of an artificial intelligence robot, wherein the artificial intelligence robot is provided with a preset number of edge nodes and one central node, each edge node is connected to two adjacent edge nodes, and each edge node is connected to the central node.

[0014] Furthermore, in step S1, the operation data is identified according to the data privacy identification algorithm to obtain private feature data and non-private feature data, and the upload time, upload method and upload location of the operation data are set, wherein:

[0015] When the operation data is private feature data, the upload time of the operation data is set to the pre-fuzzy time, the upload method of the operation data is pre-fuzzy upload, and the upload location of the operation data is the central node and the adjacent edge node;

[0016] When the operation data is non-private feature data, the upload time of the operation data is set to the current time, the upload method of the operation data is set to non-fuzzy upload, and the upload location of the operation data is set to the central node.

[0017] Furthermore, in step S2, the operation data is uploaded to the upload location for storage according to the upload time and upload method of the operation data through an asymmetric encryption method, so as to achieve separation of ownership of the operation data.

[0018] Furthermore, in step S3, the attributes of the private feature data are judged according to the location where the private feature data is generated, and the edge node private data and the central node private data are obtained. The upload time, upload method, and upload location of the private feature data are optimized according to the attributes of the private feature data, wherein:

[0019] When the private feature data attribute is edge node private data, the upload time of the private feature data is set to not upload, the upload method of the private feature data is set to not fuzzy upload, and the upload location of the private feature data is set to no upload location;

[0020] When the private feature data attribute is the central node private data, the upload time, upload method and upload location of the private feature data are not optimized.

[0021] Furthermore, in step S4, the abnormal situation of the edge node private data is judged to obtain the abnormal edge node private data and the normal edge node private data, and the optimization method for setting the upload time, upload method and upload location of the private feature data is adjusted. The abnormal edge node private data is judged as non-closed-loop reincarnation data, and is pre-fuzzy processed and uploaded. The upload location is adjusted to backup and encrypt to the adjacent edge node to achieve federated training without uploading.

[0022] Furthermore, in step S5, the storage status of the abnormal edge node private data is judged according to the number of calls and storage duration of the abnormal edge node private data, and the abnormal edge node private data is deleted according to the storage status of the abnormal edge node private data.

[0023] Furthermore, in step S6, the process of determining the storage status of the private data of the abnormal edge node is optimized according to the storage ratio of the adjacent edge nodes, thereby reducing the number of calls and storage time.

[0024] Furthermore, in step S7, the importance of the private data of the abnormal edge node is judged according to the secondary call rate of the private data of the abnormal edge node, and the upload time, upload method and upload location of the private data of the abnormal edge node in step S1 are adjusted according to the importance of the private data of the abnormal edge node. If the secondary call rate is low, it is adjusted to post-fuzzy; if the secondary call rate is high, the position is adjusted to central storage.

[0025] Furthermore, in step S7, the secondary call rate is high and the position is adjusted to be stored in the center. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of the data security protection method based on ownership separation in this embodiment. DETAILED DESCRIPTION

[0027] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0028] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] See also Figure 1 As shown, it is a flow chart of the data security protection method based on ownership separation in this embodiment, including:

[0030] Step S1: Identify the operating data according to the data privacy identification algorithm to obtain private feature data and non-private feature data, and set the upload time, upload method and upload location of the private feature data and non-private feature data;

[0031] Step S2, uploading the private feature data and the non-private feature data to an upload location for storage according to the upload time and upload method of the private feature data and the non-private feature data;

[0032] Step S3: judging the attributes of the private feature data according to the location where the private feature data is generated, obtaining edge node private data and central node private data, and optimizing the upload time, upload method, and upload location of the private feature data according to the attributes of the private feature data;

[0033] Step S4, judging the abnormality of the edge node private data, obtaining the abnormal edge node private data and the normal edge node private data, and adjusting the optimization method for setting the upload time, upload method and upload location of the private feature data;

[0034] Step S5: judging the storage status of the abnormal edge node private data according to the number of calls and storage duration of the abnormal edge node private data, and deleting the abnormal edge node private data according to the storage status of the abnormal edge node private data;

[0035] Step S6: Optimize the process of determining the storage status of private data of abnormal edge nodes based on the storage ratio of adjacent edge nodes;

[0036] Step S7: The importance of the private data of the abnormal edge node is judged according to the secondary call rate of the private data of the abnormal edge node, and the upload time, upload method and upload location of the private data of the abnormal edge node in step S1 are adjusted according to the importance of the private data of the abnormal edge node.

[0037] Specifically, this method is applied to the data processing system of the artificial intelligence robot to balance the federated training and centralized training in the artificial intelligence robot to resolve the conflicts between device computing power and encryption processing, thereby improving the efficiency of data security protection of the artificial intelligence robot.

[0038] Specifically, the data security protection method based on separation of ownership is applied to the data processing system of an artificial intelligence robot, in which a preset number of edge nodes and one central node are provided, each edge node is connected to two adjacent edge nodes, and each edge node is connected to the central node.

[0039] Specifically, in step S1, the operation data is identified according to the data privacy identification algorithm to obtain private feature data and non-private feature data, and the upload time, upload method and upload location of the operation data are set, wherein:

[0040] When the operation data is private feature data, the upload time of the operation data is set to the pre-fuzzy time, the upload method of the operation data is pre-fuzzy upload, and the upload location of the operation data is the central node and the adjacent edge node;

[0041] When the operation data is non-private feature data, the upload time of the operation data is set to the current time, the upload method of the operation data is set to non-fuzzy upload, and the upload location of the operation data is set to the central node.

[0042] Specifically, in step S2, the operation data is uploaded to the upload location for storage according to the upload time and upload method of the operation data through an asymmetric encryption method, so as to achieve separation of ownership of the operation data.

[0043] Specifically, in step S3, the attributes of the private feature data are judged according to the location where the private feature data is generated, and the edge node private data and the central node private data are obtained. The upload time, upload method and upload location of the private feature data are optimized according to the attributes of the private feature data, wherein:

[0044] When the private feature data attribute is edge node private data, the upload time of the private feature data is set to not upload, the upload method of the private feature data is set to not fuzzy upload, and the upload location of the private feature data is set to no upload location;

[0045] When the private feature data attribute is the central node private data, the upload time, upload method and upload location of the private feature data are not optimized.

[0046] Specifically, in step S4, the abnormal situation of the private data of the edge node is judged to obtain the private data of the abnormal edge node and the private data of the normal edge node, and the optimization method for setting the upload time, upload method and upload location of the private feature data is adjusted. The private data of the abnormal edge node is judged as non-closed-loop reincarnation data, and is pre-fuzzified and uploaded. The upload location is adjusted to backup and encrypt to the adjacent edge node to achieve federated training without uploading.

[0047] Specifically, in step S5, the storage status of the abnormal edge node private data is judged according to the number of calls and storage duration of the abnormal edge node private data, and the abnormal edge node private data is deleted according to the storage status of the abnormal edge node private data.

[0048] Specifically, in step S6, the process of determining the storage status of private data of abnormal edge nodes is optimized according to the storage ratio of adjacent edge nodes, thereby reducing the number of calls and storage time.

[0049] Specifically, in step S7, the importance of the private data of the abnormal edge node is judged according to the secondary call rate of the private data of the abnormal edge node, and the upload time, upload method and upload location of the private data of the abnormal edge node in step S1 are adjusted according to the importance of the private data of the abnormal edge node. If the secondary call rate is low, it is adjusted to post-fuzzy; if the secondary call rate is high, the adjusted location is centrally stored.

[0050] Specifically, in step S7, the secondary call rate is high, and the position is adjusted to be stored in the center.

[0051] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A data security protection method based on ownership separation, characterized in that: include: Step S1: Identify the operating data according to the data privacy identification algorithm to obtain private feature data and non-private feature data, and set the upload time, upload method and upload location of the private feature data and non-private feature data; Step S2, uploading the private feature data and the non-private feature data to an upload location for storage according to the upload time and upload method of the private feature data and the non-private feature data; Step S3: judging the attributes of the private feature data according to the location where the private feature data is generated, obtaining edge node private data and central node private data, and optimizing the upload time, upload method, and upload location of the private feature data according to the attributes of the private feature data; Step S4, judging the abnormality of the edge node private data, obtaining the abnormal edge node private data and the normal edge node private data, and adjusting the optimization method for setting the upload time, upload method and upload location of the private feature data; Step S5: judging the storage status of the abnormal edge node private data according to the number of calls and storage duration of the abnormal edge node private data, and deleting the abnormal edge node private data according to the storage status of the abnormal edge node private data; Step S6: Optimize the process of determining the storage status of private data of abnormal edge nodes based on the storage ratio of adjacent edge nodes; Step S7: judging the importance of the private data of the abnormal edge node according to the secondary call rate of the private data of the abnormal edge node, and adjusting the upload time, upload method and upload location of the private data of the abnormal edge node in step S1 according to the importance of the private data of the abnormal edge node; In step S1, the operation data is identified according to the data privacy identification algorithm to obtain private feature data and non-private feature data, and the upload time, upload method and upload location of the operation data are set, wherein: When the operation data is private feature data, the upload time of the operation data is set to the pre-fuzzy time, the upload method of the operation data is pre-fuzzy upload, and the upload location of the operation data is the central node and the adjacent edge node; When the operation data is non-private feature data, the upload time of the operation data is set to the current time, the upload method of the operation data is set to non-fuzzy upload, and the upload location of the operation data is set to the central node; In step S2, the operation data is uploaded to the upload location for storage according to the upload time and upload method of the operation data through an asymmetric encryption method, so as to achieve separation of ownership of the operation data.

2. The data security protection method based on ownership separation according to claim 1 is characterized in that: The data security protection method based on separation of ownership is applied to the data processing system of an artificial intelligence robot, wherein the artificial intelligence robot is provided with a preset number of edge nodes and one central node, each edge node is connected to two adjacent edge nodes, and each edge node is connected to the central node.

3. The data security protection method based on ownership separation according to claim 1 is characterized in that: In step S3, the attributes of the private feature data are judged according to the location where the private feature data is generated, and the edge node private data and the central node private data are obtained. The upload time, upload method and upload location of the private feature data are optimized according to the attributes of the private feature data, wherein: When the private feature data attribute is edge node private data, the upload time of the private feature data is set to not upload, the upload method of the private feature data is set to not fuzzy upload, and the upload location of the private feature data is set to no upload location; When the private feature data attribute is the central node private data, the upload time, upload method and upload location of the private feature data are not optimized.

4. The data security protection method based on ownership separation according to claim 1 is characterized in that: In step S4, the abnormal situation of the edge node private data is judged to obtain the abnormal edge node private data and the normal edge node private data, and the optimization method for setting the upload time, upload method and upload location of the private feature data is adjusted. The abnormal edge node private data is judged as non-closed-loop reincarnation data, and is pre-fuzzy processed and uploaded. The upload location is adjusted to backup and encrypt to the adjacent edge node to achieve federated training without uploading.

5. The data security protection method based on ownership separation according to claim 1 is characterized in that: In step S5, the storage status of the abnormal edge node private data is judged according to the number of calls and storage duration of the abnormal edge node private data, and the abnormal edge node private data is deleted according to the storage status of the abnormal edge node private data.

6. The data security protection method based on ownership separation according to claim 1 is characterized in that: In step S6, the process of determining the storage status of the private data of the abnormal edge node is optimized according to the storage ratio of the adjacent edge nodes, thereby reducing the number of calls and storage time.

7. The data security protection method based on ownership separation according to claim 1 is characterized in that: In step S7, the importance of the private data of the abnormal edge node is judged according to the secondary call rate of the private data of the abnormal edge node, and the upload time, upload method and upload location of the private data of the abnormal edge node in step S1 are adjusted according to the importance of the private data of the abnormal edge node. If the secondary call rate is low, it is adjusted to post-fuzzy; if the secondary call rate is high, the adjusted location is centrally stored.

8. The data security protection method based on ownership separation according to claim 1 is characterized in that: In step S7, the secondary call rate is high, and the position is adjusted to be stored in the center.

Images