CN114861211B - Metadata scene-oriented data privacy protection method, system and storage medium - Google Patents
Metadata scene-oriented data privacy protection method, system and storage medium Download PDFInfo
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Abstract
The invention discloses a data privacy protection method, a system and a storage medium for a meta-universe scene; the method comprises the following steps: uploading non-private data to a data storage module in the virtual world as a first local private data set to train a first local model; while private data with sensitive information is stored locally as a second local private data set training a second local model; constructing a metauniverse cross-chain federal machine learning framework, wherein the metauniverse cross-chain federal machine learning framework comprises a task publisher, a first client for training a first local model through a first local private data set, and a second client for training a second local model through a second local private data set; the task publisher and the first client are arranged in the virtual world, and the second client is arranged in the real world; the first client and the second client update and aggregate the first local model parameters and the second local model parameters based on a cross-chain aggregation method to obtain a new global model.
Description
Technical Field
The invention relates to the technical field of communication signal demodulation, in particular to a metadata scene-oriented data privacy protection method, a metadata scene-oriented data privacy protection system and a metadata scene-oriented storage medium.
Background
Federal learning (FEDERATED LEARNING) is an emerging artificial intelligence technology, and the design goal is to ensure information security during large data sharing and protect terminal data and personal data privacy; on the premise of ensuring legal compliance, high-efficiency machine learning model training is carried out among multiple participants or multiple computing nodes. The federal learning realizes the purposes of 'data availability is invisible', 'data is not out of the door' in an iterative training operation mode, and performs encryption operation on interactive data in the model training process, so that efficient model training is realized to a certain extent, the privacy of participant data is protected to the greatest extent, the problem of data privacy caused by the fact that a traditional machine learning model needs to access data is solved, and meanwhile, more organizations or institutions can be introduced for data addition, and the model quality is improved as a whole.
Blockchains, by virtue of their anonymous, non-tamperable, distributed, etc., provide a secure and reliable solution among multiple untrusted parties. The block chain is essentially a distributed account book, the biggest characteristic of the block chain is that the traditional centralization scheme is changed into a distributed network structure, and the security of data on the chain is ensured through cryptography technologies such as asymmetric encryption and the like; meanwhile, the reliability of the chain data is ensured among a plurality of untrusted distributed participants through a consensus mechanism, intelligent contracts and the like. Through authorization mechanisms, identity management and the like of the blockchain, mutually untrusted users can be integrated together as participants to establish a safe and trusted cooperation mechanism; in addition, model parameters learned by federation can be stored in a blockchain, so that the safety and reliability of the model parameters are ensured.
The current running blockchain networks are rich and various, but the blockchain networks are mutually independent, so that the interoperability among the blockchains is greatly limited. To connect different blockchain networks together to implement the value internet, blockchain cross-chain technology is critical. The whole structure of the cross-chain technology can be divided into information collection, information verification, system connection and information feedback, and information interaction between different parallel chains is realized by establishing a connecting party or expanding an intelligent contract, and after verification by two or more parties, the information interaction is recorded into a total blockchain account book. The relay chain is one of the mainstream cross-chain technologies at present, the relay chain does not completely depend on verification judgment of a trusted third party, and only data states of two block chains are collected by a middleman to carry out self verification, and the verification modes have obvious differences according to different structures of the relay chain. The relay chain is relatively large in implementation difficulty, but is perfect in function and characteristics, and has unique and important application value.
The meta universe is the next generation of internet behind the web and the mobile internet, people can freely conduct social contact, entertainment and the like in a virtual world, and people can access the meta universe in an immersive way through technologies such as VR and AR. To enhance the sense of immersion of the metauniverse, the metauniverse needs to perform continuous data synchronization, acquire fresh data from the real world, and the quantity and variety richness of personal data collected in this process will be unprecedented. For example, in VR games, in order to keep the reality of the rendered scene, the actions of the user need to be read in real time through a sensor, and meanwhile, privacy data such as physiological responses and brain electricity modes of the user may not be read with the consent of the user. How to protect the private data in the virtual world is particularly important if the entire uploaded virtual world would result in the risk of disclosure and abuse of the private data.
The disadvantages of the current prior art are as follows: the AI model in the meta-universe needs to be trained by means of data generated in the real world and the virtual world to obtain better model performance, but if the local privacy data are all uploaded to the virtual world for training, on one hand, the real world data are uploaded to the virtual world storage module, so that larger communication overhead and storage overhead exist, and on the other hand, the privacy data are also caused to have risks of privacy leakage and data abuse.
Disclosure of Invention
The invention aims to solve the problems of the defects and the shortcomings of the prior art, and provides a data privacy protection method, a system and a storage medium for a meta-universe scene.
In order to achieve the above purpose of the present invention, the following technical scheme is adopted:
a data privacy protection method facing to a meta-universe scene comprises the following steps:
Dividing data into two parts according to data types and privacy protection requirements, and directly uploading non-private data to a data storage module in a virtual world to be used as a first local private data set to train a first local model; while private data with sensitive information is stored locally as a second local private data set training a second local model;
Constructing a metauniverse cross-chain federation machine learning framework with privacy protection, wherein the metauniverse cross-chain federation machine learning framework comprises a task publisher for storing a global model, a first client for training a first local model through a first local private data set and a second client for training a second local model through a second local private data set; the task publisher and the first client are arranged in the virtual world, and the second client is arranged in the real world;
And the first client and the second client update and aggregate the first local model parameters and the second local model parameters based on a cross-chain aggregation method to obtain a new global model.
Preferably, a block chain technology is utilized to build a decentralised meta universe cross-chain federation machine learning framework, wherein one block chain is used as a main chain, and the main chain is used as a storage management module of global model parameters; a first storage module taking one blockchain as a first slave chain and taking the first slave chain as a first local model parameter; a second storage module for taking one blockchain as a second slave chain and taking the second slave chain as a second local model parameter; a relay chain is also arranged, and the relay chain is used as a cross-chain management platform;
The task publisher transmits the initialized global model through the main chain, and receives updated parameters of the first local model and the second local model through the main chain;
The first client receives the initialized global model through a first slave chain and uploads the updated first local model parameters to the first slave chain;
the second client receives the initialized global model through a second slave chain and uploads the updated second local model parameters to the second slave chain;
the relay chain realizes bridging of the first slave chain, the second slave chain and the main chain.
Further, before the meta-universe cross-chain federal machine learning framework is trained, the meta-universe cross-chain federal machine learning framework is initialized as follows:
the first slave chain and the second slave chain register to the relay chain, a task publisher determines a federal machine learning task and searches a first client and a second client which participate in federal machine learning training;
the first client and the second client determine whether to participate in a federal machine learning task according to requirements; after the two parties reach the agreement, entering a federal machine learning process;
The task publisher uploads the initialized global model w t to the main chain, and the first client of the virtual world and the second client of the real world acquire the initialized global model w t through the corresponding first slave chain and the second slave chain respectively.
Still further, the federal machine learning process is specifically as follows:
The second client and the first client receive the initialized global model w t issued by the task issuing party and initialize the global model w t respectively to obtain a second local model First local model/>The initialization process is as follows:
Wherein, Representing a second set of clients in the real world,/>A first set of clients representing a virtual world; then, the first client and the second client respectively use the first local private data set D j and the second local private data set D i for training, D i represents the second local private data set held by the real-world second client i, and D j represents the first local private data set held by the virtual world first client j; the second local model and the first local model are respectively defined as follows:
Real world:
Virtual world:
Wherein w is a global model parameter, |d i | is the size of the dataset of the second client i, |d j | is the size of the dataset of the first client j, f k (w) is a local loss function, each client performs random gradient descent for a given number of iterations, and the training results in a second local model, the first local model being as follows:
wherein, eta is the learning rate, Is a gradient.
Still further, after the first client in the virtual world and the second client in the real world train the first local model and the second local model, the trained first local model parameters and second local model parameters are respectively uploaded to the first slave chain and the second slave chain, a cross-link request is initiated on the main chain by the first slave chain and the second slave chain respectively, the first local model parameters and the second local model parameters are updated and uploaded to the main chain by a block chain cross-link mode, and the main chain verifies the received first local model parameters and second local model parameters and performs an aggregation operation to obtain a new global model w t+1, which is defined as follows:
Iterating for a certain number of times until the global model converges, and ending the federal machine learning task at the moment; the learned global model will be retained in the backbone for subsequent predictive or classification work services.
Still further, there are four participants in the cross-chain polymerization-based method, and the functions are as follows:
the verifier: the method comprises the steps of taking charge of the block out of a relay chain network, running a client of one relay chain, and verifying a block generated by a nominated blockchain;
and (3) finishing: maintaining all nodes of a blockchain, helping a verifier to collect and verify the correctness of a transaction and submitting candidate blocks to the verifier;
the nominator: the party concerned who owns the token maintains and is responsible for verifying the security of the person;
the supervisor: the benefit is obtained by the detection of illegal transactions or illegal blocks.
Still further, the cross-chain process between the second slave chain and the master chain is as follows:
s101: the second client creates an account on the second slave chain and initializes information stored on the second slave chain;
s102: after the second client finishes training the second local model, the second client creates a transaction on the second slave chain and sends the transaction including the second local model parameters and the identity information to the main chain; the transaction is a second local model parameter uploading request;
s103: the second client signs and broadcasts the transaction;
S104: collecting transaction information from a collator of the chain, verifying the validity of the transaction, collating transaction data, and packaging into candidate blocks;
s105: the collator presents the candidate block and the transition proof of state to a verifier of the second slave chain;
s106: the verifier verifies the received candidate block, only the verification passes, only effective transactions are ensured to be contained in the candidate block, and the verifier mortises the tokens of the candidate block to prepare for the uplink of the candidate block;
s107: broadcasting the candidate block to the blockchain will be authorized when there are enough nominators to mortgage their tokens and nominating the verifier;
s108: when all authenticators agree on the relay chain block, the authenticators move the transaction on the second slave chain from the exit of the second slave chain to the entrance of the master chain to complete the transmission of the message;
S109: the main chain executes the transaction in the entrance queue and modifies the account book of the main chain;
s110: the task issuing side server obtains the updated parameters of the second local model, temporarily stores the updated parameters, and performs aggregation operation after waiting for the return results of a plurality of second clients.
Still further, the first slave chain and the main chain have the following cross-chain flow path:
s201: the first client creates an account on the first slave chain and initializes information stored on the first slave chain;
S202: after the first client finishes training the first local model, the first client creates a transaction on the first slave chain and sends the first local model parameters and identity information to the main chain; the transaction is a first local model parameter uploading request;
s203: the first client signs and broadcasts the transaction;
S204: collecting transaction information by a collator of a chain, verifying the validity of a transaction, collating transaction data, and packaging into candidate blocks;
s205: the collator presents the candidate block and the transition proof of state to the verifier of the first slave chain;
S206: the verifier verifies the received candidate block, only the verification passes, only effective transactions are ensured to be contained in the candidate block, and the verifier mortises the tokens of the candidate block to prepare for the uplink of the candidate block;
S207: broadcasting the candidate block to the blockchain will be authorized when there are enough nominators to mortgage their tokens and nominating the verifier;
S208: when all authenticators agree on the relay chain block, the authenticators move the transaction on the first slave chain from the exit of the first slave chain to the entrance of the master chain to complete the transmission of the message;
s209: the main chain executes the transaction in the entrance queue and modifies the account book of the main chain;
S210: the task issuing side server obtains the updated parameters of the first local model, temporarily stores the parameters, and performs aggregation operation after waiting for the return results of a plurality of first clients.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the method as described above when said computer program is executed.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method as described above.
The beneficial effects of the invention are as follows:
The invention does not simply upload all physical data collected by the sensor directly into the virtual world, but divides the data into two parts according to the data type and the privacy protection requirement, the non-privacy data can be directly uploaded into a virtual world data storage module (such as a cloud server, an edge server and the like), and the privacy data with sensitive information is stored locally, so that the privacy protection of the artificial intelligence application in the meta-universe scene is realized.
The invention utilizes the blockchain technology to build a decentralised meta universe cross-chain federal machine learning framework, which is different from the traditional federal machine learning framework, one blockchain in the framework is called a main chain and is used as a storage management module of global model parameters, a plurality of slave chains are used as storage modules of local model parameters, and the local training model parameters are uploaded into the slave chains. The meta-universe cross-chain federal machine learning framework can record the training process of the local model, and can realize privacy protection and auditability and traceability of the local training process.
The invention also provides a cross-chain aggregation method, wherein the interaction of the local model parameters between the master chain and the slave chain is performed in a cross-chain mode, so that the traceability is ensured, and meanwhile, the safety of the interaction of the local model parameters is ensured to a certain extent.
Drawings
Fig. 1 is a schematic diagram of a data privacy protection method according to embodiment 1.
FIG. 2 is a schematic diagram of a cross-chain polymerization-based process as described in example 1.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Example 1
As shown in fig. 1, a metadata scene-oriented data privacy protection method includes the following steps:
Dividing data into two parts according to data types and privacy protection requirements, and directly uploading non-private data to a data storage module in a virtual world to be used as a first local private data set to train a first local model; while private data with sensitive information is stored locally as a second local private data set training a second local model;
Constructing a metauniverse cross-chain federation machine learning framework with privacy protection, wherein the metauniverse cross-chain federation machine learning framework comprises a task publisher for storing a global model, a first client for training a first local model through a first local private data set and a second client for training a second local model through a second local private data set; the task publisher and the first client are arranged in the virtual world, and the second client is arranged in the real world;
And the first client and the second client update and aggregate the first local model parameters and the second local model parameters based on a cross-chain aggregation method to obtain a new global model.
In a specific embodiment, a block chain technology is utilized to build a decentralised meta universe cross-chain federation machine learning framework, wherein one block chain is used as a main chain, and the main chain is used as a storage management module of global model parameters; a first storage module taking one blockchain as a first slave chain and taking the first slave chain as a first local model parameter; a second storage module for taking one blockchain as a second slave chain and taking the second slave chain as a second local model parameter; a relay chain is also arranged, and the relay chain is used as a cross-chain management platform;
The task publisher transmits the initialized global model through the main chain, and receives updated parameters of the first local model and the second local model through the main chain;
The first client receives the initialized global model through a first slave chain and uploads the updated first local model parameters to the first slave chain;
the second client receives the initialized global model through a second slave chain and uploads the updated second local model parameters to the second slave chain;
the relay chain realizes bridging of the first slave chain, the second slave chain and the main chain.
In a specific embodiment, before the meta-universe cross-chain federal machine learning framework is trained, the meta-universe cross-chain federal machine learning framework is initialized as follows:
the first slave chain and the second slave chain register to the relay chain, a task publisher determines a federal machine learning task and searches a first client and a second client which participate in federal machine learning training;
the first client and the second client determine whether to participate in a federal machine learning task according to requirements; after the two parties reach the agreement, entering a federal machine learning process;
The task publisher uploads the initialized global model w t to the main chain, and the first client of the virtual world and the second client of the real world acquire the initialized global model w t through the corresponding first slave chain and the second slave chain respectively.
In a specific embodiment, the federal machine learning process is specifically as follows:
The second client and the first client receive the initialized global model w t issued by the task issuing party and initialize the global model w t respectively to obtain a second local model First local model/>The initialization process is as follows:
Wherein, Representing a second set of clients in the real world,/>A first set of clients representing a virtual world; then, the first client and the second client respectively use the first local private data set D j and the second local private data set D i for training, D i represents the second local private data set held by the real-world second client i, and D j represents the first local private data set held by the virtual world first client j; the second local model and the first local model are respectively defined as follows:
Real world:
Virtual world:
Wherein w is a global model parameter, |d i | is the size of the dataset of the second client i, |d j | is the size of the dataset of the first client j, f k (w) is a local loss function, each client performs random gradient descent for a given number of iterations, and the training results in a second local model, the first local model being as follows:
wherein, eta is the learning rate, Is a gradient.
In a specific embodiment, after the first client in the virtual world and the second client in the real world train the first local model and the second local model, the trained first local model parameters and second local model parameters are respectively uploaded to the first slave chain and the second slave chain, a cross-link request is respectively initiated on the first slave chain and the second slave chain to the main chain, the first local model parameters and the second local model parameters are updated and uploaded to the main chain in a block chain cross-link mode, and the main chain verifies the received first local model parameters and second local model parameters and performs an aggregation operation to obtain a new global model w t+1, which is defined as follows:
Iterating for a certain number of times until the global model converges, and ending the federal machine learning task at the moment; the learned global model will be retained in the backbone for subsequent predictive or classification work services.
In a specific embodiment, the cross-chain polymerization-based method has four kinds of participants, and the functions of the four kinds of participants are as follows:
the verifier: the method comprises the steps of taking charge of the block out of a relay chain network, running a client of one relay chain, and verifying a block generated by a nominated blockchain;
and (3) finishing: maintaining all nodes of a blockchain, helping a verifier to collect and verify the correctness of a transaction and submitting candidate blocks to the verifier;
the nominator: the party concerned who owns the token maintains and is responsible for verifying the security of the person;
the supervisor: the benefit is obtained by the detection of illegal transactions or illegal blocks.
In a specific embodiment, the cross-chain process between the second slave chain and the master chain is as follows:
s101: the second client creates an account on the second slave chain and initializes information stored on the second slave chain;
s102: after the second client finishes training the second local model, the second client creates a transaction on the second slave chain and sends the transaction including the second local model parameters and the identity information to the main chain; the transaction is a second local model parameter uploading request;
s103: the second client signs and broadcasts the transaction;
S104: collecting transaction information from a collator of the chain, verifying the validity of the transaction, collating transaction data, and packaging into candidate blocks;
s105: the collator presents the candidate block and the transition proof of state to a verifier of the second slave chain;
s106: the verifier verifies the received candidate block, only the verification passes, only effective transactions are ensured to be contained in the candidate block, and the verifier mortises the tokens of the candidate block to prepare for the uplink of the candidate block;
s107: broadcasting the candidate block to the blockchain will be authorized when there are enough nominators to mortgage their tokens and nominating the verifier;
s108: when all authenticators agree on the relay chain block, the authenticators move the transaction on the second slave chain from the exit of the second slave chain to the entrance of the master chain to complete the transmission of the message;
S109: the main chain executes the transaction in the entrance queue and modifies the account book of the main chain;
s110: the task issuing side server obtains the updated parameters of the second local model, temporarily stores the updated parameters, and performs aggregation operation after waiting for the return results of a plurality of second clients.
In a specific embodiment, the first slave chain and the main chain have the following cross-chain flow path:
s201: the first client creates an account on the first slave chain and initializes information stored on the first slave chain;
S202: after the first client finishes training the first local model, the first client creates a transaction on the first slave chain and sends the first local model parameters and identity information to the main chain; the transaction is a first local model parameter uploading request;
s203: the first client signs and broadcasts the transaction;
S204: collecting transaction information by a collator of a chain, verifying the validity of a transaction, collating transaction data, and packaging into candidate blocks;
s205: the collator presents the candidate block and the transition proof of state to the verifier of the first slave chain;
S206: the verifier verifies the received candidate block, only the verification passes, only effective transactions are ensured to be contained in the candidate block, and the verifier mortises the tokens of the candidate block to prepare for the uplink of the candidate block;
S207: broadcasting the candidate block to the blockchain will be authorized when there are enough nominators to mortgage their tokens and nominating the verifier;
S208: when all authenticators agree on the relay chain block, the authenticators move the transaction on the first slave chain from the exit of the first slave chain to the entrance of the master chain to complete the transmission of the message;
s209: the main chain executes the transaction in the entrance queue and modifies the account book of the main chain;
S210: the task issuing side server obtains the updated parameters of the first local model, temporarily stores the parameters, and performs aggregation operation after waiting for the return results of a plurality of first clients.
The invention does not simply upload all physical data collected by the sensor directly into the virtual world, but divides the data into two parts according to the data type and the privacy protection requirement, the non-privacy data can be directly uploaded into a virtual world data storage module (such as a cloud server, an edge server and the like), and the privacy data with sensitive information is stored locally, so that the privacy protection of the artificial intelligence application in the meta-universe scene is realized.
The invention utilizes the blockchain technology to build a decentralised meta universe cross-chain federal machine learning framework, which is different from the traditional federal machine learning framework, one blockchain in the framework is called a main chain and is used as a storage management module of global model parameters, a plurality of slave chains are used as storage modules of local model parameters, and the local training model parameters are uploaded into the slave chains. The meta-universe cross-chain federal machine learning framework can record the training process of the local model, and can realize privacy protection and auditability and traceability of the local training process.
The invention also provides a cross-chain aggregation method, wherein the interaction of the local model parameters between the master chain and the slave chain is performed in a cross-chain mode, so that the traceability is ensured, and meanwhile, the safety of the interaction of the local model parameters is ensured to a certain extent.
Example 2
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of a data privacy protection method for a meta-cosmic scene when said computer program is executed:
Dividing data into two parts according to data types and privacy protection requirements, and directly uploading non-private data to a data storage module in a virtual world to be used as a first local private data set to train a first local model; while private data with sensitive information is stored locally as a second local private data set training a second local model;
Constructing a metauniverse cross-chain federation machine learning framework with privacy protection, wherein the metauniverse cross-chain federation machine learning framework comprises a task publisher for storing a global model, a first client for training a first local model through a first local private data set and a second client for training a second local model through a second local private data set; the task publisher and the first client are arranged in the virtual world, and the second client is arranged in the real world;
And the first client and the second client update and aggregate the first local model parameters and the second local model parameters based on a cross-chain aggregation method to obtain a new global model.
Where the memory and the processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting the various circuits of the one or more processors and the memory together. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, which further receives the data and transmits the data to the processor.
Example 3
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for protecting data privacy for a metauniverse scene:
Dividing data into two parts according to data types and privacy protection requirements, and directly uploading non-private data to a data storage module in a virtual world to be used as a first local private data set to train a first local model; while private data with sensitive information is stored locally as a second local private data set training a second local model;
Constructing a metauniverse cross-chain federation machine learning framework with privacy protection, wherein the metauniverse cross-chain federation machine learning framework comprises a task publisher for storing a global model, a first client for training a first local model through a first local private data set and a second client for training a second local model through a second local private data set; the task publisher and the first client are arranged in the virtual world, and the second client is arranged in the real world;
And the first client and the second client update and aggregate the first local model parameters and the second local model parameters based on a cross-chain aggregation method to obtain a new global model.
Those skilled in the art will appreciate that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, including instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the application. And the aforementioned storage medium includes: 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.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (10)
1. A data privacy protection method for a meta-universe scene is characterized by comprising the following steps of: the method comprises the following steps:
Dividing data into two parts according to data types and privacy protection requirements, and directly uploading non-private data to a data storage module in a virtual world to be used as a first local private data set to train a first local model; while private data with sensitive information is stored locally as a second local private data set training a second local model;
Constructing a metauniverse cross-chain federation machine learning framework with privacy protection, wherein the metauniverse cross-chain federation machine learning framework comprises a task publisher for storing a global model, a first client for training a first local model through a first local private data set and a second client for training a second local model through a second local private data set; the task publisher and the first client are arranged in the virtual world, and the second client is arranged in the real world;
The first client and the second client update and aggregate the first local model parameters and the second local model parameters based on a cross-chain aggregation method to obtain a new global model;
Constructing a decentralised meta universe cross-chain federation machine learning framework by using a blockchain technology, wherein one blockchain is used as a main chain, and the main chain is used as a storage management module of global model parameters; a first storage module taking one blockchain as a first slave chain and taking the first slave chain as a first local model parameter; a second storage module for taking one blockchain as a second slave chain and taking the second slave chain as a second local model parameter; and a relay chain is also arranged, and the relay chain is used as a cross-chain management platform.
2. The meta-universe scene-oriented data privacy protection method of claim 1, characterized by:
The task publisher transmits the initialized global model through the main chain, and receives updated parameters of the first local model and the second local model through the main chain;
The first client receives the initialized global model through a first slave chain and uploads the updated first local model parameters to the first slave chain;
the second client receives the initialized global model through a second slave chain and uploads the updated second local model parameters to the second slave chain;
the relay chain realizes bridging of the first slave chain, the second slave chain and the main chain.
3. The meta-universe scene-oriented data privacy protection method of claim 2, characterized by: before training the meta-universe cross-chain federal machine learning framework, initializing is carried out, and the method specifically comprises the following steps:
the first slave chain and the second slave chain register to the relay chain, a task publisher determines a federal machine learning task and searches a first client and a second client which participate in federal machine learning training;
the first client and the second client determine whether to participate in a federal machine learning task according to requirements; after the two parties reach the agreement, entering a federal machine learning process;
The task publisher uploads the initialized global model w t to the main chain, and the first client of the virtual world and the second client of the real world acquire the initialized global model w t through the corresponding first slave chain and the second slave chain respectively.
4. The meta-universe scene-oriented data privacy protection method of claim 3, characterized by: the federal machine learning process is specifically as follows:
The second client and the first client receive the initialized global model w t issued by the task issuing party and initialize the global model w t respectively to obtain a second local model First local model/>The initialization process is as follows:
Where P represents a second set of real-world clients, A first set of clients representing a virtual world; then, the first client and the second client respectively use the first local private data set D j and the second local private data set D i for training, D i represents the second local private data set held by the real-world second client i, and D j represents the first local private data set held by the virtual world first client j; the second local model and the first local model are respectively defined as follows:
Real world:
Virtual world:
Wherein w is a global model parameter, |d i | is the size of the dataset of the second client i, |d j | is the size of the dataset of the first client j, f k (w) is a local loss function, each client performs random gradient descent for a given number of iterations, and the training results in a second local model, the first local model being as follows:
wherein, eta is the learning rate, Is a gradient.
5. The meta-universe scene-oriented data privacy protection method of claim 4, characterized by: after the first client in the virtual world and the second client in the real world train the first local model and the second local model, the trained first local model parameters and second local model parameters are respectively uploaded to a first slave chain and a second slave chain, a cross-link request is respectively initiated on the main chain by the first slave chain and the second slave chain, the first local model parameters and the second local model parameters are updated and uploaded to the main chain in a block chain cross-link mode, and the main chain verifies the received first local model parameters and second local model parameters and performs aggregation operation to obtain a new global model w t+1, which is defined as follows:
Iterating for a certain number of times until the global model converges, and ending the federal machine learning task at the moment; the learned global model will be retained in the backbone for subsequent predictive or classification work services.
6. The meta-universe scene-oriented data privacy protection method of claim 5, characterized by: four participants are in total in the cross-chain polymerization method, and the functions of the four participants are as follows:
the verifier: the method comprises the steps of taking charge of the block out of a relay chain network, running a client of one relay chain, and verifying a block generated by a nominated blockchain;
and (3) finishing: maintaining all nodes of a blockchain, helping a verifier to collect and verify the correctness of a transaction and submitting candidate blocks to the verifier;
the nominator: the party concerned who owns the token maintains and is responsible for verifying the security of the person;
the supervisor: the benefit is obtained by the detection of illegal transactions or illegal blocks.
7. The meta-universe scene-oriented data privacy protection method of claim 6, characterized by: the process of crossing the chain between the second slave chain and the main chain is as follows:
s101: the second client creates an account on the second slave chain and initializes information stored on the second slave chain;
s102: after the second client finishes training the second local model, the second client creates a transaction on the second slave chain and sends the transaction including the second local model parameters and the identity information to the main chain; the transaction is a second local model parameter uploading request;
s103: the second client signs and broadcasts the transaction;
S104: collecting transaction information from a collator of the chain, verifying the validity of the transaction, collating transaction data, and packaging into candidate blocks;
s105: the collator presents the candidate block and the transition proof of state to a verifier of the second slave chain;
s106: the verifier verifies the received candidate block, only the verification passes, only effective transactions are ensured to be contained in the candidate block, and the verifier mortises the tokens of the candidate block to prepare for the uplink of the candidate block;
s107: broadcasting the candidate block to the blockchain will be authorized when there are enough nominators to mortgage their tokens and nominating the verifier;
s108: when all authenticators agree on the relay chain block, the authenticators move the transaction on the second slave chain from the exit of the second slave chain to the entrance of the master chain to complete the transmission of the message;
S109: the main chain executes the transaction in the entrance queue and modifies the account book of the main chain;
s110: the task issuing side server obtains the updated parameters of the second local model, temporarily stores the updated parameters, and performs aggregation operation after waiting for the return results of a plurality of second clients.
8. The meta-universe scene-oriented data privacy protection method of claim 6, characterized by: the first slave chain and the main chain have the following cross-chain flow path:
s201: the first client creates an account on the first slave chain and initializes information stored on the first slave chain;
S202: after the first client finishes training the first local model, the first client creates a transaction on the first slave chain and sends the first local model parameters and identity information to the main chain; the transaction is a first local model parameter uploading request;
s203: the first client signs and broadcasts the transaction;
S204: collecting transaction information by a collator of a chain, verifying the validity of a transaction, collating transaction data, and packaging into candidate blocks;
s205: the collator presents the candidate block and the transition proof of state to the verifier of the first slave chain;
S206: the verifier verifies the received candidate block, only the verification passes, only effective transactions are ensured to be contained in the candidate block, and the verifier mortises the tokens of the candidate block to prepare for the uplink of the candidate block;
S207: broadcasting the candidate block to the blockchain will be authorized when there are enough nominators to mortgage their tokens and nominating the verifier;
S208: when all authenticators agree on the relay chain block, the authenticators move the transaction on the first slave chain from the exit of the first slave chain to the entrance of the master chain to complete the transmission of the message;
s209: the main chain executes the transaction in the entrance queue and modifies the account book of the main chain;
S210: the task issuing side server obtains the updated parameters of the first local model, temporarily stores the parameters, and performs aggregation operation after waiting for the return results of a plurality of first clients.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, performs the steps of the method according to any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, performs the steps of the method according to any one of claims 1 to 8.
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