Education data adaptive access control method based on block chain and reputation mechanism

Abstract

The invention relates to the technical field of data sharing and access control, in particular to a block chain and reputation mechanism-based education data adaptive access control method, which comprises the following steps of: firstly, constructing a historical interaction-based credible reputation mechanism: setting default trust and reputation values for an initial visitor by an attribute mechanism; calculating an initial reputation value by combining data of other fields acquired by an oracle machine, and binding public keys and reputation information of participants, a block chain storage address and a data abstract; a data owner encrypts data by using a ciphertext policy attribute-based encryption technology, formulates a policy containing an access matrix, trust and a reputation threshold in combination with a static attribute and a reputation mechanism, and binds own public key and data information to a key list; when a visitor requests, risks are quantified, attributes and reputation are verified based on related intelligent contracts, and tokens are generated if the standards are reached, so that safe and credible sharing and dynamic control of education data are realized, and the safety and flexibility are improved.

Description

Technical Field

[0001] This invention relates to the field of data sharing and access control technology, specifically to an adaptive access control method for educational data based on blockchain and reputation mechanisms. Background Technology

[0002] With the development of educational informatization, various types of data in the teaching process (such as student grades, learning trajectories, classroom participation, and behavioral profiles) are gradually being digitized and widely applied in scenarios such as teaching evaluation, learning analysis, and personalized education. To further unlock the value of this data, more and more educational institutions and educational technology platforms are attempting to build a trusted educational data sharing environment using blockchain technology, uploading educational data to the blockchain to achieve cross-departmental and cross-platform collaborative use. However, due to the open and transparent nature of blockchain systems, shared data may be accessed by any interested third party, including some untrusted or unverified entities, posing a significant risk of privacy breaches and misuse.

[0003] Currently, access control for educational data largely relies on traditional static access control mechanisms. These mechanisms typically divide data access permissions based on preset roles and fixed policies, making it difficult to dynamically reflect the visitor's behavioral credibility and actual risk level. This static mechanism presents numerous problems when facing a large number of participants and complex collaborative scenarios. For example, malicious users may impersonate legitimate teachers or researchers to obtain sensitive student information; overly lenient access policies can easily lead to privacy leaks, while overly complex policies may result in difficulties in policy expression, high maintenance costs, and low system efficiency. Furthermore, traditional mechanisms generally lack the ability to respond to changes in visitor behavior, making them ill-suited to dynamic educational environments with evolving security requirements.

[0004] To address the limitations of static access control mechanisms, existing research has proposed risk assessment methods based on visitor reputation scores to achieve more flexible access control. These methods calculate a user's reputation score by analyzing their historical access records, thereby determining whether to grant access. However, in practical applications, reputation scores are often centrally stored and managed, posing a risk of malicious modification and lacking transparency, making it difficult to ensure the reliability of the assessment results. Furthermore, for users accessing the system for the first time, the lack of historical behavioral data makes it difficult to quantify their access risk, leading to a "cold start" problem and affecting the system's ability to fairly assess and serve new users. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive access control method for educational data based on blockchain and reputation mechanisms, which aims to alleviate the contradiction between data openness and privacy protection in educational blockchain systems and improve the security, flexibility and credibility of educational data access.

[0006] To achieve the above objectives, this invention provides an adaptive access control method for educational data based on blockchain and reputation mechanisms, comprising the following steps:

[0007] Step 1: Construct a trusted reputation mechanism based on historical interaction records;

[0008] Step 2: Cold start processing of initial visitor reputation values ​​for educational data;

[0009] Step 3: Establish a multi-layered adaptive access control mechanism based on smart contracts;

[0010] Step 4: Verification and authorization of educational data access requests based on a multi-layer adaptive access control mechanism.

[0011] Optionally, step 1, which constructs a trusted reputation mechanism based on historical interaction records, includes the following steps:

[0012] Step 1.1: Establish a trust value quantification mechanism for the initial user DU. x Obtain its trust value v in other domains through oracles. x Based on the system-defined initial trust value VE and weight coefficient ρ, where 0 < ρ < 1, calculate the initial trust value. For users with historical interactions, DU i Its trust is worth sharing. It is based on its relationship with the data owner, DO. j The calculation is based on the historical interaction records between them. Each interaction is scored according to the behavior and then processed through a recursive formula. Dynamically updated, where σ assigns higher weight to recent interactions. This represents the record of the k-th access interaction.

[0013] Step 1.2: Establish a reputation value quantification mechanism for initial users DU. x Reputation value Obtain external reputation value r through an oracle x Combining the initial reputation value (RE) and weighting coefficients calculate For historical interactive users DU i Then, sum up the positive feedback (GR) and negative feedback (BR) within the time period m to obtain the updated reputation score M. n =M o +GR+BR, then use Calculate the reputation value, where RV o This indicates the reputation feedback score previously received. Adjusting for historical influences, η1 and η2 are reward and punishment factors, and the final check... To predict access risks.

[0014] Optionally, in step 2, during the cold start processing of the initial visitor reputation value for educational data, the attribute agencies (AAs) set a default initial trust value (VE) and initial reputation value (RE) for the data visitor; the new user's trust / reputation value is obtained and calculated through an oracle; and the attribute agencies (AAs) maintain a key index table. list This binds a user's public key to their reputation value, shared data address, and data digest.

[0015] Optionally, in establishing a multi-layered adaptive access control mechanism based on smart contracts, the data owner (DO) combines predefined static policy attributes and reputation mechanisms to formulate access policies. Includes access matrix and attribute mapping, η is the trust threshold for sharing educational data (EduData). min This is the lowest reputation score; the data owner DO then binds its own public key PK. i Data storage address CTD Shared data summary D Reputation information, add to the key list. list To achieve secure and reliable sharing of educational data.

[0016] Optionally, in the process of verifying and authorizing educational data access requests based on a multi-layer adaptive access control mechanism, the data owner DO deploys two smart contracts on the blockchain platform: access control and reputation assessment. When the data accessor DU submits an access request, the reputation assessment smart contract first quantifies the access risk and binds the reputation information to the public key. Then, the access control smart contract dynamically assesses the data access based on predefined attribute access policies and the data accessor DU's reputation information. If the conditions are met, access rights are granted and an access token is issued. The data accessor DU uses this token to access the shared data and decrypts it using the attribute key to obtain the plaintext educational data.

[0017] This invention provides an adaptive access control method for educational data based on blockchain and a reputation mechanism. First, a trusted reputation mechanism based on historical interaction records is constructed. An attribute institution sets default initial trust and reputation values ​​for initial educational data visitors. An oracle is used to obtain their trust and reputation values ​​in other areas. The initial reputation value is calculated using a formula, and the public keys of system participants are bound to reputation information, the data storage address of the blockchain sharing system, and the shared data digest. Second, a multi-layered adaptive access control mechanism is built using smart contracts. Data owners encrypt educational data using ciphertext policy attribute-based encryption technology. An access policy including an access matrix, trust threshold, and reputation threshold is formulated by combining static policy attributes and the reputation mechanism. Their public keys are bound to a key list along with relevant data information. Finally, when a data visitor submits a request, the deployed reputation assessment and access control smart contract quantifies access risk, verifies visitor attributes and reputation information, and generates an access token if the conditions are met. This achieves secure and trusted sharing and dynamic access control of educational data, effectively improving the security and flexibility of educational data sharing. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an adaptive access control method for educational data based on blockchain and reputation mechanisms according to the present invention.

[0020] Figure 2 This is a schematic diagram of the model architecture of a specific embodiment of the present invention. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0022] Please see Figure 1 This invention provides an adaptive access control method for educational data based on blockchain and reputation mechanisms, comprising the following steps:

[0023] S1: Construct a trusted reputation mechanism based on historical interaction records;

[0024] S2: Cold start processing of initial visitor reputation values ​​for educational data;

[0025] S3: Establish a multi-layered adaptive access control mechanism based on smart contracts;

[0026] S4: Educational data access request verification and authorization based on a multi-layer adaptive access control mechanism.

[0027] The following provides further explanation in conjunction with the implementation steps and specific embodiments:

[0028] like Figure 2 The diagram shown is a model architecture diagram of an embodiment. Specifically, an adaptive access control method for educational data based on blockchain and reputation mechanisms according to the present invention includes the following steps:

[0029] 1) Trustworthy reputation mechanism based on historical interaction records:

[0030] 1.1) Quantification of trust value of data visitors:

[0031] For initial user DU x By introducing an oracle mechanism, which uses a user's trust value from other domains as their initial trust value, not only can the reliability of the trust value be ensured, but the access risk of new users can also be effectively quantified, thus solving the cold start problem for initial users. DU x Trust is worth sharing The calculation formula (1) is as follows:

[0032]

[0033] Among them, v x Represents the DU obtained through the oracle x The reputation score ρ in another specific area is based on DU. x A weighting coefficient (0 < ρ < 1) is set for access behavior in other domains, and VE is the initial trust value defined in the system.

[0034] For data visitors with historical interaction records, DU i DU i Trust is worth all based on its relationship with the data owner, DO. j The reputation score is calculated based on historical interactions between the parties. After t interactions, the reputation score is... The calculation formula is:

[0035]

[0036] This formula represents the data visitor DU with historical records. iThe trust value calculation process. Here, σ represents the weight parameter value (0 < σ < 1), and the parameter value σ is dynamically adjusted so that the latest interaction record has a higher weight value than the older interaction record. Let k represent the record of the k-th access interaction. If the k-th access interaction is an honest behavior and conforms to the access policy, it is denoted as k. If the k-th access interaction is malicious, then... in, and This makes building trust more difficult than losing it, making it more suitable for real-world applications. In extreme cases, such as when DO... j When dealing with a completely untrusted entity, meaning countless interactions have occurred and all interaction logs show malicious activity, then DU... i The trust value is represented as Similarly, if DO j It is a completely trustworthy entity, and all interaction records show honest behavior. In this case, DU i The trust value is recorded as DU i The trust value is limited to the following range:

[0037]

[0038] Using the aforementioned trust value calculation method, the DoS (Doubt) of data visitors is quantified. j Access behavior and determine DO j Whether the trust value falls within this range is used to predict the access risk. Furthermore, the calculation process for this trust value score follows the following recursive rule:

[0039]

[0040] 1.2) Quantification of reputation scores for data visitors:

[0041] For the initial user, the reputation value of that user in other areas is obtained through an oracle and used as the initial reputation value. Initial user DU x Its reputation deserves to be divided. The calculation formula is as follows:

[0042]

[0043] Where, r x Represents the initial user DU obtained through the oracle. x Reputation value in another field It is based on DU x A weighting coefficient set for access behavior in other domains RE is the initial reputation value defined in this system.

[0044] For data visitors with historical interaction records, DU i DU i The reputation score is based on the data owner (DO) with whom the data was interacted. j It is calculated from the historical interaction records between them. First, it is necessary to summarize the interactions with DU within the time period m. i All DOs that interact j Reputation feedback yields an updated reputation score M. n The calculation formula is as follows:

[0045] M n =M o +GR+BR,(6)

[0046] Where GR and BR represent DU i The positive and negative feedback received within the time interval m, M o Representing DU i The previous total number of feedback. Secondly, based on the updated number of reputation feedback, calculate DU. i Reputation value

[0047] Among them, RV o This indicates the reputation feedback score previously received. This is a weighting parameter used to adjust the degree to which recent historical interactions influence the current reputation calculation. Specifically, the influence of historical reputation on the current reputation calculation gradually weakens over time; the longer the time period, the less significant the influence. This is where the weighting parameter comes in. Subsequently, the impact of historical reputation on the current reputation calculation decreases gradually, thus highlighting the importance of recent historical interaction records. η1 and η2 represent the reward factor and penalty factor, respectively. Finally, a threshold η is defined. min The reputation of the inspection deserves recognition. Does it meet the requirements? This is used to predict the access risk of data owners (DU) i .

[0048] 2) Cold start processing of initial visitor reputation values ​​for educational data:

[0049] First, the attribute agencies (AAs) set a default initial trust value (VE) and an initial reputation value (RE) for the data visitor. Second, they obtain the user's trust value (v) in other domains through oracles. x and reputation value r x Finally, the initial user's trust and reputation scores are obtained using the following formulas (8) and (9):

[0050]

[0051] Where, ρ and They are based on DU x A weighting coefficient is set for access behavior in other domains, and 0 < ρ < 1. In addition, AAs defines a key index table. list All participants in the system, including data owners and data accessors, bind their public keys to their corresponding reputation information, as well as their data storage addresses and shared data digests on the blockchain sharing system.

[0052] 3) Multi-layer adaptive access control mechanism based on smart contracts:

[0053] During the educational data sharing phase, the data owner (DO) uses ciphertext policy attribute-based encryption technology to encrypt the educational data, ensuring data privacy and defining access policies to prevent unauthorized malicious access. At the same time, the user's public key is bound to the data storage address, shared data digest, and reputation information on the blockchain sharing system.

[0054] When data owners (DOs) need to share data, a multi-layered adaptive access control mechanism is required. This mechanism should constrain access users based on predefined static access policies and dynamically block access from users with low reputation values ​​who match the access policy attributes. Therefore, DOs need to design a smart contract for access control based on this mechanism to achieve secure and reliable sharing of educational data. First, DOs formulate an access policy by combining predefined static policy attributes and a reputation mechanism. The definition is as follows:

[0055]

[0056] Among them, access structure It is an l1×l2 access matrix, mapping function Access Matrix The rows in the attribute set are associated with the attribute values ​​in the attribute set. and These are the minimum and maximum trust values ​​for the shared data (Data). Data users (DUs) must fall within these thresholds to satisfy their access requirements for the shared data. Additionally, the DO specifies a threshold η. min This serves as the minimum reputation score for sharing data (Data). Secondly, DO needs to PK its own public key. i Data storage address on the blockchain sharing system CTD Shared data summary D Bind it to reputation information. Then, PK this public key. i Add relevant information to the key list list The formula is as follows:

[0057] key list (PK i ) = Address CTD share D ,V,R,(11)

[0058] Where V represents the user's trust value and R represents the user's reputation value.

[0059] 4) Educational data access request verification and authorization based on a multi-layer adaptive access control mechanism:

[0060] Data owner DO deployed two smart contracts on the blockchain platform: an access control smart contract and a reputation assessment smart contract. When data visitor DU submits an access request transaction, firstly, the reputation assessment smart contract quantifies DU's access risk and then compares DU's reputation information with its public key. j The process is interconnected. First, by executing the access control smart contract in response to the user's request, the access behavior is dynamically evaluated based on predefined access policies and the user's reputation information, thereby granting the appropriate access permissions. Finally, upon successful authorization, the access control smart contract issues an access token. The DU can use this token to access shared data, and through the DU's attribute key SK... su Decrypt to obtain plaintext data. The specific algorithm of the smart contract is described below.

[0061] The reputation assessment smart contract aims to quantitatively assess the access risk of a visitor (DU) based on their historical access records. This assessment system primarily comprises two core risk quantification indicators: trust value and reputation value. First, the trust value and reputation value of the DU are quantified through a trusted reputation mechanism based on historical interaction records. For initial users without established historical interaction records, their access behavior is difficult to quantify directly. Therefore, oracle technology is introduced to obtain the user's trust value (v) in other areas. x and reputation value r x This serves as the user's initial reputation score. Furthermore, the system predefines the user's initial trust value (VE) and initial reputation value (RE). For users with historical interaction records, their trust and reputation scores are calculated based on their historical interactions with the data owner (DO). Subsequently, the system uses DU's public key (PK). j It is bound to its reputation information and stored in the key list. list middle:

[0062] key list (PK j =Null,Null,V,R.(12)

[0063] Where V represents DU's trust value and R represents DU's reputation value.

[0064] The smart contract grants access to the data owner DO's data through an access request submitted by the data accessor DU. This request includes DU's public key PK. j and attribute key SK Su The smart contract will assess the risk of DU's access attempt and grant the corresponding access permissions. First, it will determine whether DU's attributes meet the attribute set defined in the access policy, and then, based on DU's public key PK... j The obtained reputation score is used to further determine whether the DU falls within the legal access range. If the DU meets the above access requirements, the DO will authorize the DU to perform the access operation. Subsequently, the access smart contract will issue an access token {Token = time, limit}, where time is the token's timestamp and limit is the token's validity period. The DU can use this token to access shared data. All operations of this access are recorded in a distributed ledger, ensuring the openness and transparency of the access process.

[0065] The above description discloses only one or more preferred embodiments of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. An adaptive access control method for educational data based on blockchain and reputation mechanisms, characterized in that, Includes the following steps: Step 1: Construct a trusted reputation mechanism based on historical interaction records; Step 2: Cold start processing of initial visitor reputation values ​​for educational data; Step 3: Establish a multi-layered adaptive access control mechanism based on smart contracts; Step 4: Verification and authorization of educational data access requests based on a multi-layer adaptive access control mechanism.

2. The adaptive access control method for educational data based on blockchain and reputation mechanisms as described in claim 1, characterized in that, Step 1, the process of building a trusted reputation mechanism based on historical interaction records, includes the following steps: Step 1.1: Establish a trust value quantification mechanism for the initial user DU. x Obtain its trust value v in other domains through oracles. x Based on the system-defined initial trust value VE and weight coefficient ρ, where 0 < ρ < 1, calculate the initial trust value. For users with historical interactions, DU i Its trust is worth sharing. It is based on its relationship with the data owner, DO. j The calculation is based on the historical interaction records between them. Each interaction is scored according to the behavior and then processed through a recursive formula. Dynamically updated, where σ assigns higher weight to recent interactions. This represents the record of the k-th access interaction. Step 1.2: Establish a reputation value quantification mechanism for initial users DU. x Reputation value Obtain external reputation value r through an oracle x Combining the initial reputation value (RE) and weighting coefficients calculate For historical interactive users DU i Then, sum up the positive feedback (GR) and negative feedback (BR) within the time period m to obtain the updated reputation score M. n =M o +GR+BR, then use Calculate the reputation value, where RV o This indicates the reputation feedback score previously received. Adjusting for historical influences, η1 and η2 are reward and punishment factors, and the final check... To predict access risks.

3. The adaptive access control method for educational data based on blockchain and reputation mechanisms as described in claim 2, characterized in that, Step 2: During the cold start process of initial visitor reputation values ​​for educational data, Attribute Agencies (AAs) set default initial trust values ​​(VE) and initial reputation values ​​(RE) for data visitors; new user trust / reputation values ​​are obtained and calculated via an oracle; and Attribute Agencies (AAs) maintain a key index table. list This binds a user's public key to their reputation value, shared data address, and data digest.

4. The adaptive access control method for educational data based on blockchain and reputation mechanisms as described in claim 3, characterized in that, In establishing a multi-layered adaptive access control mechanism based on smart contracts, the data owner (DO) combines predefined static policy attributes and a reputation mechanism to formulate access policies. Includes access matrix and attribute mapping, η is the trust threshold for sharing educational data (EduData). min This is the lowest reputation score; the data owner DO then binds its own public key PK. i Data storage address CTD Shared data summary D Reputation information, add to the key list. list To achieve secure and reliable sharing of educational data.

5. The adaptive access control method for educational data based on blockchain and reputation mechanisms as described in claim 4, characterized in that, In the process of verifying and authorizing educational data access requests based on a multi-layered adaptive access control mechanism, the data owner DO deploys two smart contracts on the blockchain platform: access control and reputation assessment. When the data accessor DU submits an access request, the reputation assessment smart contract first quantifies the access risk and binds the reputation information to the public key. Then, the access control smart contract dynamically assesses the data access based on predefined attribute access policies and the data accessor DU's reputation information. If the conditions are met, access rights are granted and an access token is issued. The data accessor DU uses this token to access the shared data and decrypts it using the attribute key to obtain the plaintext educational data.

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