Medical place automatic door data sharing and privacy protection system based on block chain

Abstract

The invention discloses a block chain-based medical place automatic door data sharing and privacy protection system, which is characterized by comprising an edge sensing module used for processing biological recognition data through edge nodes, outputting desensitization feature vectors and packaging device fingerprints and timestamps; the encryption storage module is used for dynamically generating an encryption key, performing anonymous permission verification in combination with zero-knowledge proof, encrypting data and performing fragmentation storage on the key; the evidence storage and sharing module is used for constructing an alliance chain and carrying out evidence storage on the desensitized and encrypted data through a multi-channel uplink, and nodes on the alliance chain are in data intercommunication through a cross-chain protocol; the authority management module is used for deploying a three-layer intelligent contract and automatically executing authority control according to an alliance chain setting rule; and the double-chain auditing module is used for automatically detecting an abnormal access behavior by adopting a double-chain structure and automatically triggering an intelligent contract to adjust a permission strategy. According to the method, the data management mode of the medical place is reconstructed, and the method has remarkable advantages in compliance, efficiency improvement and emergency response dimensions.

Description

Technical Field

[0001] This invention relates to the field of automatic door technology, specifically a blockchain-based data sharing and privacy protection system for automatic doors in medical facilities. Background Technology

[0002] Traditional automated door data sharing relies on wired or Wi-Fi transmission to a centralized server in the hospital's information system for centralized processing. The raw biometric data lacks real-time anonymization mechanisms during transmission and storage, making it vulnerable to server attacks or internal leaks, directly exposing patient identity information. It depends on the stability of the hospital's intranet, and transmission delays may occur during peak outpatient hours, impacting access control efficiency. Furthermore, it fails to record unique device identifiers and operation timestamps, making it difficult to trace data sources and operation records, and posing a risk of repudiation. Traditional data sharing depends on centralized system integration, achieving cross-system data interoperability through HL7, FHIR protocols, or direct database connections, but requires significant development resources to adapt to different vendors' systems. The lack of tamper-proof records during data sharing makes it difficult to prove data integrity in the event of medical disputes. Inconsistent data formats and standards among different hospitals further complicate data sharing within medical consortia.

[0003] Traditional automatic door privacy protection systems employ static encryption keys and role-based access control. Encryption keys are generated and stored centrally by the hospital's IT department, posing a single point of failure risk. Access control configuration requires manual maintenance and cannot dynamically respond to business changes. Access verification relies on usernames, passwords, or IC cards, failing to provide anonymous access. Traditional auditing relies on log systems, requiring significant manpower for access control and data compliance checks. While centralized log platforms collect device logs, this only enables post-event analysis and cannot block abnormal access in real time. Furthermore, logs stored on local servers are susceptible to tampering. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention adopts the following technical solution: a blockchain-based automatic door data sharing and privacy protection system for medical facilities, characterized in that it includes:

[0005] The edge sensing module is used to process biometric data through edge nodes, output desensitized feature vectors, and encapsulate device fingerprints and timestamps;

[0006] The encrypted storage module is used to dynamically generate encryption keys, combine zero-knowledge proofs for anonymous access verification, encrypt data, and store keys in fragments.

[0007] The evidence sharing module is used to build a consortium blockchain, which stores de-identified and encrypted data on the blockchain through multiple channels, and allows nodes on the consortium blockchain to communicate with each other through cross-chain protocols.

[0008] The permission management module is used to deploy three-layer smart contracts and automatically execute permission control according to the rules set by the consortium blockchain;

[0009] The dual-chain audit module is used to automatically detect abnormal access behavior and automatically trigger smart contracts to adjust permission policies when using a dual-chain structure.

[0010] The edge node processing specifically involves: using a lightweight machine learning model to extract key dimensions of biometric features, including face and fingerprint, locally at the edge node; hashing and desensitizing the feature vector to output a desensitized feature vector; and encapsulating the desensitized feature vector, device fingerprint, and timestamp into an edge data unit, which is then temporarily stored in a local security chip, awaiting the generation of an encryption key by the encryption storage module.

[0011] The method for dynamically generating encryption keys is as follows: a two-factor generation strategy using timestamps and random entropy sources is adopted. Each time an edge node requests encryption, the hardware security module extracts a true random number from the environmental noise, combines it with the current timestamp to generate an initial key seed, and expands it into a data encryption key and an authentication key through an encryption algorithm.

[0012] The anonymous permission verification using zero-knowledge proof is as follows: when a user submits an access request to an edge node, the zk-SNARKs algorithm is called to generate proof parameters, and the proof process does not reveal specific permission details; the edge node first verifies the validity of the SEK by associating the timestamp with the device fingerprint, and then calls the consortium blockchain through a smart contract to verify the on-chain record of the user's permissions. After the double verification is passed, the access control is unlocked.

[0013] The key fragment storage refers to dividing the data encryption key DEK into n fragments and sending them to access controllers, edge nodes, and blockchain gateways respectively. A reconstruction threshold k is set, requiring at least k fragments to reconstruct the key.

[0014] The construction of the consortium blockchain refers to the following: medical institutions, regulatory agencies, and auditing agencies apply to join the consortium blockchain through CA certificates, and the node identity ID and institution code are registered on the blockchain; the master node collects the encrypted data sent by the edge nodes and broadcasts it to all consensus nodes; the nodes verify the digital signature, and after more than 2 nodes reach a consensus, it is packaged into a block.

[0015] The multi-channel on-chain evidence storage refers to: classifying data according to priority; in the emergency channel, rescue events are triggered and the data is uploaded to the chain in real time, skipping batch packaging and being verified first; in the ordinary channel, daily traffic data is packaged into a transaction group of 50 items, compressed through zero-knowledge proofs, and then uploaded to the chain.

[0016] The three-layer smart contract is as follows: the data asset and identity authentication contract only allows authenticated entities to call the contract interface, and the initial permissions are preset by the administrator; the permission rules and logic execution contract receives access requests from the upper-layer application, calls the underlying identity contract to verify the entity's identity, generates permission decisions according to preset rules, and records the decision log on the blockchain; the application interface and interaction contract integrate homomorphic encryption technology in the data sharing interface, allowing the receiver to perform calculations on encrypted data, while simultaneously accessing the blockchain's access control log.

[0017] The automatic triggering of smart contract permission adjustment strategy means that the system analyzes sidechain logs in real time through machine learning algorithms to automatically identify abnormal access patterns; once a risk is detected, the smart contract automatically triggers permission downgrade or upgrade and updates the main chain permission strategy in sync.

[0018] Combining all the above technical solutions, the positive effects of this invention are as follows: 1. This invention generates de-identified feature vectors in real time through edge nodes, ensuring that the original biometric information does not leave the collection device, thus cutting off the privacy leakage path at the source. Zero-knowledge proofs enable the system to verify permissions without obtaining the user's real identity. A key sharding algorithm is adopted, combined with distributed storage of consortium blockchain nodes, so even if some nodes are attacked, attackers cannot obtain the complete key. A three-tiered protection mechanism—source de-identification, anonymous verification, and distributed encryption—is constructed for the full lifecycle protection of biometric data.

[0019] 2. This invention constructs a decentralized, trusted, and shared network through consortium blockchain notarization and cross-chain interoperability. The consortium blockchain features multi-channel on-chain storage, such as transaction chain notarization of operation records and data chain notarization of anonymized features, leveraging the immutability of blockchain to ensure full traceability of data sharing. Cross-chain protocols enable data interoperability between consortium blockchains; Hospital B can verify Hospital A's notarized patient biometric data via smart contracts, eliminating the need for repeated collection.

[0020] 3. This invention deploys smart contracts in the rule layer, execution layer, and verification layer to automatically adjust permissions according to the rules of the consortium blockchain. It adopts a dual-chain structure with the main chain storing normal operations and the audit chain monitoring abnormal behavior. Through AI anomaly detection models, such as LSTM-based access pattern learning, risky behaviors are identified in real time, and smart contracts are automatically triggered to freeze permissions and store them on the blockchain, thereby increasing security and improving real-time risk response efficiency.

[0021] 4. This invention utilizes distributed storage across consortium blockchain nodes, eliminating the need for dedicated maintenance of centralized servers and reducing hardware costs. Simultaneously, the blockchain's consensus mechanism ensures data consistency, minimizing access control system outages caused by server failures. This technological innovation not only restructures the data governance model for medical facilities but also demonstrates significant advantages in compliance, efficiency improvement, and emergency response. Attached Figure Description

[0022] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0023] Figure 1 This is a system framework diagram of the system of the present invention.

[0024] Figure 2 This is a flowchart illustrating the execution strategy for abnormal behavior risk levels in this invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] See Figure 1 As shown, this invention proposes a blockchain-based data sharing and privacy protection system for automatic doors in medical facilities, including an edge sensing module, an encrypted storage module, an evidence sharing module, an access control module, and a dual-chain auditing module.

[0027] In a more specific application of this invention, the edge sensing module serves as the system's data entry point, undertaking the tasks of real-time processing and secure encapsulation of biometric data. The edge computing architecture adopts a distributed deployment mode of terminal devices and edge nodes. The terminal device layer integrates biometric sensors such as infrared cameras, capacitive fingerprint scanners, and iris scanners, supporting multimodal data acquisition; the edge node layer deploys lightweight edge computing devices, configured with ARM architecture processors and hardware security modules, supporting localized real-time computing and key generation.

[0028] Biometric terminals are installed at the entrances of medical facilities, such as outpatient halls and ICU corridors, and connected to edge nodes via wired or wireless connections. Upon startup, the edge node performs triple verification: device fingerprint registration (e.g., submitting the hardware ID to the consortium blockchain and obtaining a digital certificate), time synchronization calibration, and algorithm model loading, including a pre-trained feature extraction model. When a user triggers biometric recognition, such as facial recognition or fingerprint scanning, the terminal sensor transmits raw data, such as the RGB matrix of the face image and the grayscale image of the fingerprint, to the edge node. The edge node performs real-time processing in memory, which may involve: locating biometric regions (e.g., detecting confidence levels for faces) using object detection algorithms such as YOLOv8n and cropping irrelevant backgrounds; generating desensitized features using corresponding algorithms (e.g., face recognition time, fingerprint recognition time), and immediately clearing the raw data from memory after processing; verifying the validity of the feature vectors, and triggering re-collection if the data is invalid. Biometric desensitization algorithms employ a two-layer processing mechanism of feature extraction and de-identification. Feature extraction utilizes specialized algorithms for different biometric data types: face recognition uses a lightweight CNN model to extract 128-dimensional feature vectors, calculates key point coordinates such as the corners of the eyes and the tip of the nose, and converts them into relative distance matrices, removing identifiable information such as skin color and hairstyle; fingerprint recognition extracts minutiae features through a Gabor filter, generating an 8x8 binary feature matrix, discarding the fingerprint image itself; iris recognition uses a Daugman rubber sheet model to extract phase information, generating a 2048-bit iris code, filtering out the absolute position data of the iris texture. The feature vectors are hashed and a random salt value is added to ensure one hash per data type, preventing the feature vectors from being reverse-engineered.

[0029] The de-identified feature vector, device fingerprint, and timestamp are encapsulated into a Protocol Buffers format data packet, and a digital signature is added to the edge node to ensure that the data has not been tampered with. The encapsulated data is then transmitted to the encrypted storage module via a message queue. When the edge node detects a network interruption, it enables local caching and retransmits the data in chronological order after the network is restored to ensure that no data is lost. Regular health checks are performed on the edge nodes, including computing power utilization, temperature, and remaining storage space. In case of anomalies, automatic restart or remote maintenance is triggered.

[0030] The edge-aware module, through its design of local processing, real-time desensitization, and trusted encapsulation, constructs the first line of security at the source of biological data, laying a crucial foundation for subsequent encrypted storage and on-chain evidence preservation. Its technical implementation ensures efficient access to medical facilities while physically eliminating the risk of biometric leakage.

[0031] The encrypted storage module is the core hub for the system to achieve data security. Its technical design revolves around three major functions: dynamic key generation, anonymous permission verification, and segmented secure storage.

[0032] The method for dynamically generating encryption keys is as follows: During system runtime, the hardware security module of the edge node first collects environmental radio frequency noise, such as circuit thermal noise and electromagnetic radiation fluctuations, to generate a true random entropy source, ensuring unpredictability at the physical layer. Synchronously acquired Unix timestamps provide dynamic variables in the time-series dimension.

[0033] Two-factor data is hashed and fused using the SM3 national cryptographic algorithm to generate a 256-bit initial key seed. This seed is then expanded using AES-256-CBC mode: the first round of encryption outputs a 512-bit intermediate key, which is then used by a key derivation function to separate the data encryption key and the authentication key. The entire process is completed within the secure enclave of HSM, ensuring that key materials are never leaked into the general memory area.

[0034] The anonymous permission verification using zero-knowledge proofs works as follows: During initial registration, the user's biometric hash is bound to their DID. Permission information is deployed on the consortium blockchain in the form of a smart contract, such as an ICU access permission contract, specifying parameters like validity period and authorized department. The user submits their biometrics via a terminal. The edge node generates a de-identified feature F and triggers an encryption module, which calls a zk-SNARKs generator, inputting the user's DID and the current operation context, such as the target access control ID and request time T. The generated proof π, along with the device fingerprint D and timestamp T, is encapsulated into a verification packet and transmitted to the edge node via the TLS 1.3 protocol. The edge node calculates the expected SEK value using AVK and compares it with the SEK in the verification packet, while simultaneously checking if the timestamp T is within the allowable error range of the device clock. A JSON-RPC call is made to the consortium blockchain node, where the smart contract parses the proof π to verify if the user's DID is in the target access control's permission list and if the permission has not expired. After both verifications are successful, the edge node sends an unlock command to the access control controller. This command includes the MAC code of the timestamp T and the device fingerprint D to prevent tampering.

[0035] Key sharding storage refers to dividing the data encryption key DEK into n shards, which are then sent to access controllers, edge nodes, and blockchain gateways. A reconstruction threshold k is set, requiring at least k shards to reconstruct the key. The shard hash value and shard index are stored in the consortium blockchain transaction to generate an immutable storage certificate. The sharded data is encrypted and distributed among the edge nodes of three different institutions. The storage nodes support encrypted retrieval using homomorphic encryption technology.

[0036] The evidence storage and sharing module serves as the core hub of the system, enabling trusted evidence storage and cross-institutional circulation of medical data through a consortium blockchain architecture. This module employs multi-channel on-chain processing, cross-chain interoperability, and distributed indexing technologies to construct a high-throughput, low-latency medical data sharing network.

[0037] Building a consortium blockchain involves the following steps: Medical institutions, regulatory agencies, and auditing firms first apply to join the consortium blockchain using CA certificates. Each institution submits qualification certificates to the root CA, and upon approval, generates an X.509v3 digital certificate containing a public key and institution code information. Subsequently, the institution encapsulates the certificate information into a registration transaction and submits it to the consortium blockchain. After smart contract verification, a node identity ID bound to the institution code is generated and uploaded to the blockchain. The master node is selected from the consensus nodes using a round-robin mechanism, collects EDU encrypted data sent by edge nodes, verifies the device fingerprint, timestamp, and digital signature, and then broadcasts the data to all consensus nodes. Nodes verify the digital signature, and when more than 2 / 3 of the nodes reach consensus, the master node packages the data into a block, including block header, body, and footer information, completing the on-chain notarization.

[0038] Multi-channel on-chain notarization constructs differentiated processing paths based on data priority. The emergency channel is designed specifically for emergency-level data: when an edge node detects an emergency event (such as an abnormal opening of an emergency room access control system), it immediately encapsulates the EDU data and attaches a high-priority identifier, bypassing the batch packaging process. The master node prioritizes verifying the signature and timeliness, directly broadcasting it to the consensus node. PBFT fast consensus is used, requiring only 1 / 2 node confirmation, ensuring rapid on-chain notarization. The normal channel handles daily traffic data: edge nodes cache data according to time windows or quantity thresholds, call a zero-knowledge proof generator to compress transaction information, and the master node aggregates it and packages it according to the standard PBFT consensus process (2 / 3 node confirmation), reducing the block generation cycle and increasing throughput.

[0039] At the technical level, consortium blockchain cross-chain technology can construct a two-layer cross-chain bridging system: the relay layer deploys relay nodes operated by neutral institutions, achieving dynamic routing and encrypted message transmission based on the PolkadotXCMP protocol, supporting efficient forwarding of cross-chain messages; the contract layer deploys cross-chain adaptation contracts to complete data format conversion and transaction coordination between different chains. Threshold signatures and Merkle proof technologies are employed to ensure the credibility and integrity of cross-chain data sources, and a unified data format for medical cross-chain data units is defined, including source chain ID, target chain ID, data hash, and key information for proof of existence.

[0040] During implementation, when a node in Institute A initiates a cross-chain request, it first verifies the authorization using zero-knowledge proof, and then forwards the request to the target chain via the relay node network. After retrieving the data, Institute B generates a Merkle proof, which is then voted on by the relay nodes and encrypted before being transmitted back to Institute A's chain. After verifying the signature and proof, the recipient decrypts the data using its local key, ensuring that the entire interaction is traceable. If a timeout or verification failure occurs, a three-retry mechanism is automatically triggered. If the retry still fails, the exception is recorded and the regulator is notified.

[0041] In the access control module, the three-layer smart contract consists of: the data asset and identity authentication contract, which only allows authenticated entities to call the contract interface, with initial permissions preset by the administrator; and the access rules and logic execution contract, which receives access requests from the upper-layer application, calls the underlying identity contract to verify the entity's identity, generates access decisions based on preset rules, and records the decision log on the blockchain. The decision generation process involves: calling the underlying identity contract to verify the validity of the requester's DID; parsing the request context, including the target access control ID, current time, and device geolocation, and matching it with preset rules; and generating an access decision with supporting evidence.

[0042] The application interface and interaction contract integrate homomorphic encryption technology in the data sharing interface, allowing the receiver to perform calculations on encrypted data. They also integrate the Paillier homomorphic encryption algorithm and embed encryption functions in the data sharing interface, allowing the receiver to perform statistical calculations without decryption, while simultaneously using blockchain access control logs.

[0043] The dual-chain audit module employs a main chain for evidence storage and an audit chain for monitoring, enabling automatic detection of abnormal behavior and dynamic adjustment of permissions. The main chain, based on Hyperledger Fabric, stores access logs containing user DID, device ID, and timestamps to ensure high throughput. The audit chain, based on Ethereum, deploys anomaly detection smart contracts and an LSTM model, and is bidirectionally anchored to the main chain via sidechain technology. The module combines a rules engine and an AI model to analyze access patterns and detect abnormal behavior in real time.

[0044] Reference Figure 2 As shown, the risk levels are divided into: Level 1 risk is triggered by a single routine behavioral feature, Level 2 risk is caused by the intersection of multiple behavioral features and anomalies, and Level 3 risk is caused by triggering a preset key behavioral feature.

[0045] The sidechain anomaly detection contract executes corresponding strategies based on preset anomaly risk levels: Level 1 risk only records audit logs to the sidechain, triggering the permission management module to generate an early warning notification, such as an SMS reminder to the administrator; Level 2 risk automatically calls the permission smart contract to temporarily downgrade the permissions of the involved DID, such as downgrading ICU access to a regular ward, valid for 4 hours, and generates a permission adjustment transaction on the main chain; Level 3 risk immediately freezes all permissions of the involved DID, synchronizes the main chain permission policy to prohibit access, and sends a cross-chain alert to the regulatory node. The permission adjustment result is synchronized to the main chain via cross-chain messages. After the main chain smart contract verifies the sidechain Merkle proof, it updates the global permission table and broadcasts it to all consensus nodes.

[0046] This mechanism combines deep pattern analysis from machine learning with the immutable evidence storage of blockchain to achieve a leap from post-audit to real-time defense. At the same time, it protects the privacy of training data through federated learning technology, ensuring that algorithm iteration does not leak sensitive information, and builds a self-evolving intelligent protection system for medical data security.

[0047] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0048] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A blockchain-based data sharing and privacy protection system for automatic doors in medical facilities, characterized in that: include: The edge sensing module is used to process biometric data through edge nodes, output desensitized feature vectors, and encapsulate device fingerprints and timestamps; The encrypted storage module is used to dynamically generate encryption keys, combine zero-knowledge proofs for anonymous access verification, encrypt data, and store keys in fragments. The evidence sharing module is used to build a consortium blockchain, which stores de-identified and encrypted data on the blockchain through multiple channels, and allows nodes on the consortium blockchain to communicate with each other through cross-chain protocols. The permission management module is used to deploy three-layer smart contracts and automatically execute permission control according to the rules set by the consortium blockchain; The dual-chain audit module is used to automatically detect abnormal access behavior and automatically trigger smart contracts to adjust permission policies when using a dual-chain structure.

2. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The edge node processing specifically involves using a lightweight machine learning model to extract key dimensions of biometric features locally at the edge node, including face and fingerprint. Perform hash-based desensitization on the feature vector and output the desensitized feature vector; The desensitized feature vector, device fingerprint, and timestamp are encapsulated into edge data units and temporarily stored in the local security chip, waiting for the encryption storage module to generate the encryption key.

3. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The method for dynamically generating encryption keys is as follows: a two-factor generation strategy using timestamps and random entropy sources is adopted. Each time an edge node requests encryption, the hardware security module extracts a true random number from the environmental noise, combines it with the current timestamp to generate an initial key seed, and expands it into a data encryption key and an authentication key through an encryption algorithm.

4. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The anonymous permission verification using zero-knowledge proof is as follows: when a user submits an access request to an edge node, the zk-SNARKs algorithm is called to generate proof parameters, and the proof process does not reveal specific permission details; the edge node first verifies the validity of the SEK by associating the timestamp with the device fingerprint, and then calls the consortium blockchain through a smart contract to verify the on-chain record of the user's permissions. After the double verification is passed, the access control is unlocked.

5. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The key fragment storage refers to dividing the data encryption key DEK into n fragments and sending them to access controllers, edge nodes, and blockchain gateways respectively. A reconstruction threshold k is set, requiring at least k fragments to reconstruct the key.

6. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The construction of the consortium blockchain refers to the following: medical institutions, regulatory agencies, and auditing agencies apply to join the consortium blockchain through CA certificates, and the node identity ID and institution code are registered on the blockchain; the master node collects the encrypted data sent by the edge nodes and broadcasts it to all consensus nodes; the nodes verify the digital signature, and after more than 2 nodes reach a consensus, it is packaged into a block.

7. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The multi-channel on-chain evidence storage refers to: classifying data according to preset priorities; in the emergency channel, rescue events trigger real-time on-chain storage, skipping batch packaging and prioritizing verification; in the ordinary channel, daily traffic data is packaged into a transaction group of 50 items, compressed through zero-knowledge proofs, and then uploaded to the chain.

8. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The three-layer smart contract is as follows: the data asset and identity authentication contract only allows authenticated entities to call the contract interface, and the initial permissions are preset by the administrator; the permission rules and logic execution contract receives access requests from the upper-layer application, calls the underlying identity contract to verify the entity's identity, generates permission decisions according to preset rules, and records the decision log on the blockchain; the application interface and interaction contract integrate homomorphic encryption technology in the data sharing interface, allowing the receiver to perform calculations on encrypted data, while simultaneously accessing the blockchain's access control log.

9. The blockchain-based automatic door data sharing and privacy protection system for medical facilities as described in claim 1, characterized in that: The automatic triggering of smart contract permission adjustment strategy means that the system analyzes sidechain logs in real time through machine learning algorithms to automatically identify abnormal access patterns; once a risk is detected, the smart contract automatically triggers permission downgrade or upgrade and updates the main chain permission strategy in sync.

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