Cross-platform equity joint data security sharing method

Abstract

The invention relates to the technical field of private data security sharing, and discloses a cross-platform rights and interests joint data security sharing method, which comprises the following steps: generating a timestamp based on a data fragment corresponding to a user rights and interests related data fragment, and carrying out time attenuation and private sketch mapping processing; performing two rounds of independent blinding processing on the low-dimensional private sketch by using the platform side blinding factor and the collaborative side blinding factor; performing consistency aggregation processing on the double blind sketches from the same user main body by using a verifiable threshold aggregation protocol; and generating a main body identity verification token of the consistency aggregation result by adopting a service identity generation algorithm, and carrying out cross-platform data security sharing. According to the method, on the premise of not exposing original rights and interests data, time perception modeling, privacy protection mapping and consistency judgment are carried out on the rights and interests of the cross-platform user main body, a verifiable main body identity verification token is generated, and cross-platform rights and interests updating and data security sharing are achieved.

Description

Technical Field

[0001] This invention relates to the field of secure sharing of privacy data, and more particularly to a method for secure sharing of cross-platform rights-based collaborative data. Background Technology

[0002] With the rapid evolution and increasing complexity of user benefits systems across multiple platforms (points, coupons, membership levels, benefit redemption records, etc.), benefit data is exhibiting a trend towards fragmentation, multi-temporal generation, and distributed generation. Significant differences exist across platforms in business touchpoints, data field design, storage strategies, and identity identification systems, resulting in the benefit information of the same user often existing as weakly correlated fields without continuous identification chains. To conduct cross-platform joint risk control, mutual recognition of benefits, joint marketing, and user authenticity verification, all parties need to share and aggregate this fragmented information while ensuring privacy and security.

[0003] However, existing privacy-sharing mechanisms mainly rely on federated computing, homomorphic encryption, and multi-party secure computation to perform cryptographic evaluation of "complete data or aligned identifiers," making it difficult to achieve subject consistency confirmation when identifiers cannot be directly connected and field fragments occur at different times. More complexly, data fragments such as user behavior trajectories, coupon trigger events, and points changes often exhibit time sensitivity and weak correlation. Without secure aggregation and correlation inference capabilities, different platforms will be unable to establish trusted user identity mappings, leading to gaps in mutual recognition of rights, misjudgments in risk control, or privacy leaks.

[0004] Existing research includes patents such as CN114969710B, which proposes a method, apparatus, and device for sharing benefits. This method obtains the benefit duration purchased by a first user and corresponding sharing parameters (including sharing duration, number of users, and sharing location), generates a benefit password and benefit link, and sends them to a second user. The second user then initiates a group joining application based on the password or link. The system decides whether to join the benefit group based on a benefit screening strategy, thereby achieving the sharing of benefit duration. This method achieves the transfer or sharing of benefits on a single platform or under an explicit authorization relationship, reducing manual configuration costs. However, its technical implementation relies on explicit benefit identification, password transmission, and group management, essentially remaining a centralized, plaintext-traceable benefit distribution model, making it difficult to apply to joint benefit identification scenarios across multiple platforms.

[0005] To address this issue, this invention proposes a cross-platform data security sharing method for rights and interests. This method achieves aggregation and joint updating of rights and interests among user entities without exposing plaintext identifiers or revealing associated paths. It not only fills a gap in the existing privacy sharing system but also forms a key foundation for realizing the trusted transfer of rights and interests among multiple parties and data security collaboration. It has significant research value and practical significance for promoting the interconnection of cross-platform digital rights and interests ecosystems and the practical application of privacy protection technologies. Summary of the Invention

[0006] This invention proposes a cross-platform rights and interests joint data security sharing method. Step S1 introduces timeliness awareness through time decay processing to reduce historical noise, and compresses high-dimensional, identifiable rights and interests data fragments into irreversible low-dimensional representations through private sketch mapping, achieving both user private information protection and cross-platform comparability. Step S2 uses two independent rounds of blinding on the platform side and blinding on the collaboration side to block the reverse inference capability of a single platform or single role on the low-dimensional private sketch without destroying sketch dimension alignment and symbol consistency, thereby improving anti-collusion and anti-re-identification capabilities. Step S3 utilizes a verifiable threshold aggregation protocol to complete consistency calculation under double blinding conditions. Only when the aggregation results of multiple platforms meet the threshold constraints is a valid consistent aggregation result formed, avoiding single-point judgment and false association. Step S4 maps the consistent aggregation result into an irreversible, time-controlled subject authentication token, realizing secure sharing of data in place of the token. This enables the rights and interests business side to complete authentication, rights triggering, and rule execution without accessing any original or aggregated rights and interests data fragments.

[0007] To achieve the above objectives, this invention provides a cross-platform rights-sharing data security sharing method, comprising the following steps:

[0008] S1: Collect user rights-related data fragments across platforms, generate timestamps based on the data fragments corresponding to the user rights-related data fragments, perform time decay and private sketch mapping processing on the user rights-related data fragments, and obtain a low-dimensional private sketch with time awareness and protection of user private information.

[0009] S2: The low-dimensional private sketch is subjected to two rounds of independent blinding processing using the platform-side blinding factor and the collaborative-side blinding factor to obtain a double-blinded sketch.

[0010] S3: Based on the double-blinded sketch, perform consistency aggregation processing on the double-blinded sketches from the same user subject using a verifiable threshold aggregation protocol to obtain a consistent aggregation result for the same user subject;

[0011] S4: Based on the consistent aggregation result, a business identity generation algorithm is used to generate a principal authentication token for the consistent aggregation result, and cross-platform data security sharing is performed based on the principal authentication token.

[0012] As a further improvement of the present invention:

[0013] Further, in step S1, user rights-related data fragments are collected across platforms, timestamps are generated based on the data fragments corresponding to the user rights-related data fragments, and time decay is performed on the user rights-related data fragments, including:

[0014] S11: Each business platform collects user rights-related data fragments under its corresponding platform and generates timestamps for the data fragments corresponding to the user rights-related data fragments. The user rights-related data fragments represent data units formed by users on different business platforms and at different times that can indirectly reflect their rights status or rights usage behavior.

[0015] S12: Map the user rights-related data fragments into a numerical vector form to obtain a user rights-related data fragment vector;

[0016] S13: Convert the timestamp of the data segment corresponding to the user rights-related data segment into a time decay coefficient;

[0017] S14: Concatenate the time decay coefficient with the corresponding user rights-related data segment vector to obtain the time decay processing result of the user rights-related data segment.

[0018] Further, step S1 performs private sketch mapping processing on the time decay processing result of the user rights-related data segment to obtain a low-dimensional private sketch with time awareness and user privacy information protection, and also includes:

[0019] S15: Perform a standardization process on the time decay processing result of the user rights-related data segment by padding the ends with 0, to obtain a standardized vector of fixed length;

[0020] S16: Perform an irreversible compression mapping on the normalized vector to obtain a low-dimensional private sketch of fixed length.

[0021] Furthermore, the compression mapping formula in step S16 is:

[0022] ;

[0023] ;

[0024] ;

[0025] in, Represents a standardized vector. Represents a standardized vector Len vector values ​​in Represents a standardized vector The first in A vector value, Represents a standardized vector The first in A vector value, Len represents the fixed length of the normalized vector. This indicates that the projection vector corresponding to the nth private sketch dimension is in the nth dimension. Projection coefficients on each feature N represents the number of dimensions of the private sketch, and s represents the normalized vector. The compressed mapping result, where the compressed mapping result is in N-dimensional vector form. The compression mapping results are represented in sequence. The compressed projection values ​​of the first to Nth dimensions, Represents the compression mapping result The compressed projection value of the nth dimension. Represents a symbolic function.

[0026] Furthermore, in step S2, the low-dimensional private sketch is subjected to two rounds of independent blinding processing using a platform-side blinding factor and a collaborative-side blinding factor, including:

[0027] S21: Construct platform-side blinding factor and collaborative-side blinding factor, where the platform-side blinding factor is a platform-private mask vector form, and the collaborative-side blinding factor is a platform-wide common offset vector form;

[0028] S22: The low-dimensional private sketch is subjected to the first round of collaborative blinding processing using the collaborative side blinding factor to obtain the first blinded sketch.

[0029] S23: The first blinded sketch is subjected to a second round of platform blinding processing using the platform-side blinding factor to obtain a double-blinded sketch.

[0030] Furthermore, the generation process of the platform-side blinding factor is as follows:

[0031] S211: Construct a common discriminant index function cluster, which contains several common discriminant index functions with the same number of dimensions as the private sketch;

[0032] S212: Extract platform features and use the public discriminant index function to map the platform features into a platform-side blinding factor, wherein the length of the platform-side blinding factor is consistent with the number of dimensions of the private sketch.

[0033] Furthermore, step S3 utilizes a verifiable threshold aggregation protocol to perform consistent aggregation processing on double-blinded sketches from the same user subject, including:

[0034] S31: Calculate the similarity between different double-blinded sketches within the same platform, and classify double-blinded sketches with similarity higher than a preset similarity threshold into the same sketch cluster, wherein the double-blinded sketches in the same sketch cluster belong to the same user subject under the same platform;

[0035] S32: Perform weighted consistency aggregation on the double blinded sketches in the sketch cluster to obtain the aggregated blinded sketch of the user subject on the corresponding platform;

[0036] S33: Calculate the consistency of the cross-platform aggregated blind sketch. If the consistency meets the verifiable threshold aggregation protocol, then perform cross-platform aggregation on the cross-platform aggregated blind sketch to obtain a consistent aggregation result from the same user subject.

[0037] Further, in step S4, the principal authentication token of the consistent aggregation result is generated using a business identity generation algorithm, including:

[0038] S41: Obtain the valid timestamp range for the principal authentication token;

[0039] S42: Extract all platform business type codes of the platform associated with the consistent aggregation result, and concatenate all platform business type codes to form the platform business type code vector of the consistent aggregation result;

[0040] S43: The consistent aggregation result, the effective timestamp range, and the platform business type encoding vector are concatenated, and the concatenated result is mapped to a fixed-length subject authentication token using a dual mapping function that combines a hash function and a pseudo-random function.

[0041] Furthermore, the cross-platform data security sharing based on the principal authentication token in step S4 also includes:

[0042] S44: The subject authentication token is sent to the rights and benefits service terminal. The rights and benefits service terminal verifies the valid timestamp range in the subject authentication token. If the current timestamp is within the valid timestamp range, the verification is successful, and the user subject associated with the subject authentication token is allowed to participate in the execution process of cross-platform joint rights and benefits, and the process proceeds to step S45; otherwise, the verification fails, and the user subject associated with the subject authentication token is not allowed to participate in the execution process of cross-platform joint rights and benefits.

[0043] S45: Generate the rights enforcement rule of the subject authentication token based on the business rule derived key, and send the subject authentication token and the rights enforcement rule of the subject authentication token to the platform associated with the subject authentication token;

[0044] S46: The platform sends business rights to the user entity associated with the entity's identity verification token in accordance with the rights execution rules.

[0045] Compared with existing technologies, this invention proposes a cross-platform rights-sharing data security sharing method, which has the following beneficial effects:

[0046] First, this invention standardizes and irreversibly maps user rights-related data fragments after time decay processing to a private sketch, effectively improving privacy protection while ensuring the feasibility of cross-platform rights joint calculation. Specifically, the invention first standardizes the time decay processing results by padding the ends with zeros, ensuring that time decay processing results from different platforms and of different types are represented in a unified fixed-length space, avoiding joint calculation bias caused by inconsistent dimensions. Then, it employs an irreversible compression mapping method based on random projection and sign functions to compress high-dimensional, associative rights vectors into a fixed-dimensional low-dimensional private sketch. This significantly reduces the possibility of a single platform or third party reversing the original rights data, time distribution, or rights structure from the sketch. The resulting low-dimensional private sketch maintains the overall distribution characteristics and relative geometric structure of individual rights while masking specific vector values ​​and source paths, enabling cross-platform consistency verification to be completed solely based on the sketch, avoiding the privacy leakage risks associated with plaintext data sharing. Thus, this invention achieves a computable and irreversible joint representation of rights, providing a secure, stable, and efficient input foundation for subsequent consistency verification, threshold aggregation, and business identity token generation.

[0047] Meanwhile, this invention unifies the aggregation result of the user subject's consistency, the effective timestamp range of the subject authentication token, and the cross-platform platform business type encoding vector by concatenating them, and generates a fixed-length subject authentication token using a dual mapping mechanism that integrates hash functions and pseudo-random functions. This achieves the joint binding of consistency, timeliness, and business semantics of the user subject's cross-platform identity. Specifically, by incorporating the effective timestamp range into the generation of the subject authentication token, the token inherently possesses timeliness control capabilities, preventing replay and abuse risks arising from the long-term validity of the token. Furthermore, by embedding the platform business type encoding vector into the token derivation process, the generated subject authentication token is strongly associated with specific business scenarios, preventing illegal reuse across business domains. Moreover, by hashing first and then mapping using a pseudo-random function, an irreversible and unassociatable fixed-length subject authentication token can be generated without exposing the original aggregation result and business characteristics, balancing security, privacy protection, and cross-platform verification consistency. This approach is suitable for business scenarios such as joint rights, joint risk control, and cross-platform data sharing. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a cross-platform rights-sharing data security sharing method according to an embodiment of the present invention.

[0049] Figure 2 This invention provides a cross-platform rights-sharing data flow diagram as an embodiment of the present invention. Detailed Implementation

[0050] The realization of the objectives, functional characteristics, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0051] This invention provides a method for secure sharing of cross-platform equity-sharing data. The executing entity of this method includes, but is not limited to, at least one of the following: a server, a terminal, or other electronic device configured to execute the method provided in this invention. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, where the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0052] Reference Figure 1 as well as Figure 2 Embodiment 1 of the present invention is as follows:

[0053] S1: Collect user rights-related data fragments across platforms, generate timestamps based on the data fragments corresponding to the user rights-related data fragments, perform time decay and private sketch mapping processing on the user rights-related data fragments, and obtain a low-dimensional private sketch with time awareness and protection of user private information.

[0054] Collect user rights-related data fragments across platforms, generate timestamps based on the data fragments corresponding to the user rights-related data fragments, and perform time decay on the user rights-related data fragments, including:

[0055] S11: Each business platform collects user rights-related data fragments under its corresponding platform and generates timestamps for the data fragments corresponding to the user rights-related data fragments. The user rights-related data fragments represent data units formed by users on different business platforms and at different times that can indirectly reflect their rights status or rights usage behavior.

[0056] Specifically, the user rights-related data fragments include at least one of the following types: rights acquisition or change record fragments (such as qualification generation, status activation, level change); rights usage behavior fragments (such as redemption, cancellation, usage frequency); rights attribute feature fragments (such as rights category, validity period, quota range).

[0057] S12: Map the user rights-related data fragments into a numerical vector form to obtain a user rights-related data fragment vector;

[0058] Optionally, the mapping method for the user rights-related data fragments is either a one-hot encoding method after word segmentation or an encoding method based on a word vector model (such as the BERT model or the bag-of-words model);

[0059] S13: Convert the timestamp of the data segment corresponding to the user rights-related data segment into a time decay coefficient;

[0060] Specifically, the time decay coefficient conversion formula for the timestamp generation of the data segment is as follows:

[0061] ;

[0062] in, This indicates that a data segment generates a timestamp. Indicates the timestamp generated by the data segment The time decay coefficient obtained from the conversion, This indicates the attenuation control parameter, and the settings are as follows: It is 0.2. Indicates the time stamp generated from the data segment The most recent reference time point, and ;

[0063] It should be noted that each platform periodically collects user rights-related data fragments from its corresponding platform, as well as the timestamps generated by the corresponding data fragments. At a reference time point in each period, a principal authentication token suitable for joint data security sharing in the current period is generated. The time interval between adjacent reference time points is one day. Specifically, this invention initiates joint data security sharing at 00:00 on August 3, 2025, and collects user rights-related data fragments from each platform over the past year, as well as the timestamps generated by the corresponding data fragments, to generate a corresponding principal authentication token. The generated principal authentication token can be used for joint data security sharing from 00:00 to 23:59 on August 3, 2025. However, starting from 00:00 on August 4, 2025, the principal authentication token generated on August 3, 2025, will not be able to be used for joint data security sharing.

[0064] S14: Concatenate the time decay coefficient with the corresponding user rights-related data segment vector to obtain the time decay processing result of the user rights-related data segment.

[0065] It should be noted that this invention achieves a temporal characterization and unified expression of user rights status without relying on plaintext identity identifiers by vectorizing and modeling cross-platform user rights-related data. On the one hand, it maps user rights-related data fragments from different platforms and of different types into a unified numerical vector form, improving the comparability and fusion of heterogeneous user rights-related data fragments during joint processing. On the other hand, it introduces a timestamp-based time decay coefficient, causing the influence of historical user rights-related data fragments on current identity verification and rights judgment to naturally weaken over time, reducing the interference of outdated data fragments on the joint results, thereby improving the accuracy and timeliness of cross-platform consistency verification. Furthermore, this invention generates subject identity verification tokens with validity periods at fixed reference time points, achieving periodic updates and automatic expiration control of rights joint results, effectively reducing the privacy leakage risk caused by long-term association, and enhancing the controllability, security, and business applicability of the joint data security sharing process.

[0066] Step S1 performs private sketch mapping processing on the time decay processing result of the user rights-related data segment to obtain a low-dimensional private sketch with time awareness and user privacy information protection, and also includes:

[0067] S15: Perform a standardization process on the time decay processing result of the user rights-related data segment by padding the ends with 0, to obtain a standardized vector of fixed length;

[0068] Specifically, the fixed length of the normalized vector is set to Len (which can be set to 15), and Len is greater than the length of the decay result at any time.

[0069] S16: Perform an irreversible compression mapping on the normalized vector to obtain a low-dimensional private sketch of fixed length.

[0070] The compression mapping formula in step S16 is:

[0071] ;

[0072] ;

[0073] ;

[0074] in, Represents a standardized vector. Represents a standardized vector Len vector values ​​in Represents a standardized vector The first in A vector value, Represents a standardized vector The first in A vector value, Len represents the fixed length of the normalized vector. This indicates that the projection vector corresponding to the nth private sketch dimension is in the nth dimension. Projection coefficients on each feature N represents the number of dimensions of the private sketch, and s represents the normalized vector. The compressed mapping result, where the compressed mapping result is in N-dimensional vector form. The compression mapping results are represented in sequence. The compressed projection values ​​of the first to Nth dimensions, Represents the compression mapping result The compressed projection value of the nth dimension. Represents a symbolic function.

[0075] Specifically, different platforms should use the same set of projection coefficients or those derived from the same master key when generating low-dimensional private sketches, and regenerate the projection coefficients each time joint data security sharing is initiated. The projection coefficients must meet the conditions of zero mean, finite variance and statistical independence to ensure the randomness and anti-reconstruction ability of the projection results, while ensuring a stable and consistent mapping effect on the same or highly similar input vectors.

[0076] Specifically, the low-dimensional private sketch employs a random projection and symbol quantization mechanism under unified projection coefficients. Essentially, it determines the symbol of the original standardized vector across multiple random hyperplanes. When standardized vectors from different platforms for the same subject exhibit consistency in overall distribution and direction, the resulting low-dimensional private sketch, after mapping with the same projection coefficients, demonstrates high consistency in symbol results across multiple dimensions. This consistency can be calculated using Hamming distance, symbol matching rate, or threshold consistency rules. Because the symbol function discards amplitude information and disrupts linear invertibility, the sketch itself cannot reverse-engineer the original vector, but it still retains the ability to distinguish between similar (same symbol) and dissimilar (symbol divergence).

[0077] S2: The low-dimensional private sketch is subjected to two rounds of independent blinding processing using the platform-side blinding factor and the collaborative-side blinding factor to obtain a double-blinded sketch.

[0078] The low-dimensional private sketch is subjected to two rounds of independent blinding processing using platform-side blinding factors and collaborative-side blinding factors, including:

[0079] S21: Construct platform-side blinding factor and collaborative-side blinding factor, where the platform-side blinding factor is a platform-private mask vector form, and the collaborative-side blinding factor is a platform-wide common offset vector form;

[0080] S22: The low-dimensional private sketch is subjected to the first round of collaborative blinding processing using the collaborative side blinding factor to obtain the first blinded sketch.

[0081] Specifically, the formula for the first round of collaborative blinding processing is:

[0082] ;

[0083] ;

[0084] ;

[0085] in, Representing a low-dimensional private sketch The first layer of blind sketch, This represents the known collaborative blinding factor across the entire platform. These represent the values ​​of the first to Nth cooperative blinding factors, respectively. Representing a low-dimensional private sketch The result of the first level of collaborative blinding processing of the compressed projection value;

[0086] S23: The first blinded sketch is subjected to a second round of platform blinding processing using the platform-side blinding factor to obtain a double-blinded sketch.

[0087] As an embodiment of the present invention, the formula for performing a second round of platform blinding processing on the first blinded sketch using the platform-side blinding factor is as follows:

[0088] ;

[0089] ;

[0090] in, Represents the first level of blinding sketch The results of the second round of platform blinding processing Indicates platform-side blinding factor The nth platform blinding factor value, This indicates a placeholder state that will not participate in the consistency evaluation. express The results of the second round of platform blinding processing.

[0091] It should be noted that this invention employs a dual-round blinding mechanism combining platform-side blinding factors and collaborative-side blinding factors to achieve layered protection and structural alignment of low-dimensional private sketches. Specifically, the first round of collaborative blinding uses a platform-wide common offset vector to perform a consistent and controllable perturbation on the low-dimensional private sketch, ensuring that the low-dimensional private sketches output by each platform maintain isomorphism in the statistical space while effectively masking the true distribution of their compressed projection values. The second round of platform blinding introduces a platform-specific mask vector to place and mask dimensions that do not participate in cross-platform discrimination, further isolating platform-difference features without changing the overall dimensional length of the sketch, thus preventing cross-platform reverse inference. This method, while ensuring structural consistency and dimensional semantic alignment of low-dimensional private sketches across different platforms, achieves a discriminative but irreversible joint representation, balancing privacy protection strength with the feasibility of consistent computation.

[0092] The process for generating the platform-side blinding factor is as follows:

[0093] S211: Construct a common discriminant index function cluster, which contains several common discriminant index functions with the same number of dimensions as the private sketch;

[0094] Specifically, the common discriminant index function is an irreversible mapping function (such as a random mapping function or a hash mapping function) used to map input features to the range of 0-1. The common discriminant index function family is publicly available on all platforms, and its representation is as follows: ,in This represents the nth common discriminant index function in the cluster of common discriminant index functions;

[0095] S212: Extract platform features and use the public discriminant index function to map the platform features into a platform-side blinding factor, wherein the length of the platform-side blinding factor is consistent with the number of dimensions of the private sketch.

[0096] Specifically, the platform features include platform business type coding and equity category distribution summaries, and the platform side-blinding factor is represented as follows: ,in Indicates the platform-side blinding factor. Indicates platform-side blinding factor The blinding factor value of the nth platform:

[0097] ;

[0098] in, Indicates platform characteristics, This represents the overall platform characteristics, which include the coding of the business type that accounts for the largest proportion across the entire platform and a summary of the distribution of rights categories across the entire platform.

[0099] It should be noted that this invention constructs a public discriminative index function cluster that is open to all platforms and has consistent dimensions. This enables different platforms to generate structurally aligned platform-side blinding factors based on a unified discriminative space without exchanging any data, thereby ensuring the consistency of subsequent private sketch blinding results in terms of dimensional length and positional semantics. Simultaneously, this invention uses platform business type encoding and rights category distribution summaries as platform feature inputs and introduces platform-wide features as a common reference benchmark. This achieves relative discriminative analysis and difference characterization of platform features, avoiding direct exposure of single-platform features and enhancing privacy protection capabilities. Furthermore, by using an irreversible mapping function to map platform features to 0-1 platform-side blinding factors, it effectively reduces the risk of platform features being reverse-inferred or reconstructed, while not affecting the consistent discriminative and aggregation calculations of the same entity across different platforms, thus improving data security sharing and discriminability in cross-platform collaborative scenarios.

[0100] S3: Based on the double-blinded sketch, perform consistency aggregation processing on the double-blinded sketches from the same user subject using a verifiable threshold aggregation protocol to obtain a consistent aggregation result for the same user subject.

[0101] Consistent aggregation of double-blinded sketches from the same user subject is performed using a verifiable threshold aggregation protocol, including:

[0102] S31: Calculate the similarity between different double-blinded sketches within the same platform, and classify double-blinded sketches with similarity higher than a preset similarity threshold into the same sketch cluster, wherein the double-blinded sketches in the same sketch cluster belong to the same user subject under the same platform;

[0103] Specifically, the similarity calculation formula between different double-blinded sketches within the same platform is as follows:

[0104] ;

[0105] in, Represents a double-blinded sketch within the same platform. Similarity between them This represents a discriminant function. If the expression in the discriminant function is true, the discriminant function outputs 1; otherwise, it outputs 0. express The discriminant for whether the signs are consistent, if If the signs are the same, then the discriminant If it is true, then the discriminant is true; otherwise, the discriminant is true. This is an False value, where the sign indicates the positive or negative value. Represents a double-blinding sketch The sketch value in the nth sketch dimension, Represents a double-blinding sketch The sketch value in the nth sketch dimension, where the sketch dimension corresponds one-to-one with the dimension of the compression mapping result, and the nth sketch dimension corresponds to the nth dimension in the compression mapping result;

[0106] S32: Perform weighted consistency aggregation on the double blinded sketches in the sketch cluster to obtain the aggregated blinded sketch of the user subject on the corresponding platform;

[0107] Specifically, the weighted consistency aggregation formula for the double-blinded sketches in the sketch cluster is as follows:

[0108] ;

[0109] ;

[0110] ;

[0111] in, Representing sketch clusters The corresponding user subject in the sketch cluster The corresponding platform's aggregated blind sketch, , Representing sketch clusters Any double-blinded sketch in the image, Represents a double-blinding sketch The weighting coefficients, Represents a double-blinding sketch The time decay coefficient of the corresponding user rights-related data segment. Represents a double-blinding sketch In the sketch cluster The stability coefficient in Representing sketch clusters The number of aggregated blind sketches in the middle, Represents a double-blinding sketch and Similarity between them Representing sketch clusters Any double-blinded sketch in, and ;

[0112] S33: Calculate the consistency of the cross-platform aggregated blind sketch. If the consistency meets the verifiable threshold aggregation protocol, then perform cross-platform aggregation on the cross-platform aggregated blind sketch to obtain a consistent aggregation result from the same user subject.

[0113] Specifically, the consistency calculation formula for the cross-platform aggregated blinded sketch is as follows:

[0114] ;

[0115] in, Represents a cross-platform aggregation blinded sketch Consistency These represent the aggregation blinding sketches respectively. The sketch value in the nth sketch dimension, This represents the logical AND operator;

[0116] like If the value is higher than a preset verifiable threshold (e.g., 0.6), it indicates that the aggregated blinded sketch has been generated. Satisfy the verifiable threshold aggregation protocol and blind the aggregation sketch. As a clustered blind sketch of the same user subject;

[0117] Furthermore, the consistency calculation of the cross-platform aggregated blinded sketch is only performed in a common comparable subspace where the dimensions of both sketches are not masked, avoiding interference from invalid dimensions. The consistency result is given in the form of a ratio and compared with a preset threshold. Cross-platform aggregation is only allowed when the threshold condition is met, thus ensuring that aggregation is only performed when a majority of the consistency conditions are met. At the same time, the aggregation determination process can be independently recalculated by a third-party module based on the same formula. The aggregation conditions are clear and the results can be repeatedly verified, thus possessing threshold constraints and verifiability.

[0118] Overlay all aggregated blind sketches of the same user subject to obtain a consistent aggregated result for the user subject.

[0119] Optionally, during the overlay process of the aggregated blind sketch, the placeholder state is overlaid as a null value.

[0120] It should be noted that this invention, by introducing a verifiable threshold aggregation protocol, performs a layered processing flow of intra-platform similarity merging, cross-platform consistency verification, and subject-level aggregation on double-blinded sketches, effectively solving the problem that cross-platform rights data is difficult to jointly identify the same user subject under strong privacy constraints. Specifically, at the platform level, clustering of double-blinded sketches is performed based on a similarity function that relies solely on symbol consistency, avoiding any explicit exposure of original data, timestamp information, or platform identifiers. This enables automatic merging of sketches from the same platform and subject without unblinding. At the platform-level aggregation stage, a weighted consistency aggregation mechanism that considers both time decay coefficients and stability coefficients is introduced. This ensures that recent and stable rights behaviors contribute more to the user subject representation and significantly suppresses the interference of occasional noise or abnormal behavior on the aggregation results. At the cross-platform level, consistency calculation constrained by placeholder status and verifiable threshold determination are used to verify the consistency of aggregated blinded sketches only on the common valid dimension. This ensures the comparability of sketches output from different platforms while avoiding the leakage of any cross-platform association paths or source information. Under the premise of maintaining the irreversibility of sketches, verifiable aggregation and consistency determination of the same subject are achieved.

[0121] Furthermore, the consistent aggregation result of user subjects is an irreversible joint representation at the subject level formed after time decay, private sketch mapping, and double blinding processing of rights and interests data fragments across multiple platforms and time periods. This representation does not correspond to the original data fragments of any single platform, nor does it contain specific rights and interests content, timestamps, or behavioral paths. Instead, it reflects the consistent recognition results of different platforms for the same user subject in a statistical sense. Its essence is the consensus fingerprint of the same user subject reached by multiple platforms under the condition of minimal information exposure. It can serve as a secure input basis for business identity token generation, cross-platform rights and interests collaboration, joint risk control, or consistent decision-making, while naturally blocking reverse restoration and cross-platform association attacks.

[0122] S4: Based on the consistent aggregation result, a business identity generation algorithm is used to generate a principal authentication token for the consistent aggregation result, and cross-platform data security sharing is performed based on the principal authentication token.

[0123] The principal authentication token for the consistent aggregation result is generated using a business identity generation algorithm, including:

[0124] S41: Obtain the valid timestamp range for the principal authentication token;

[0125] S42: Extract all platform business type codes of the platform associated with the consistent aggregation result, and concatenate all platform business type codes to form the platform business type code vector of the consistent aggregation result;

[0126] S43: The consistent aggregation result, the effective timestamp range, and the platform business type encoding vector are concatenated, and the concatenated result is mapped to a fixed-length subject authentication token using a dual mapping function that combines a hash function and a pseudo-random function.

[0127] Specifically, user subject The formula for generating the principal authentication token is:

[0128] ;

[0129] in, Representing the user subject The main authentication token, Representing the user subject Consistent aggregation results Indicates the valid timestamp range of the principal authentication token. This indicates the consistent aggregation result. Platform business type encoding vector, This indicates a splicing operation. This represents a one-way hash function. This represents a pseudo-random function.

[0130] The S4 step, which involves cross-platform data security sharing based on the principal authentication token, also includes:

[0131] S44: The subject authentication token is sent to the rights and interests service terminal. The rights and interests service terminal verifies the valid timestamp range in the subject authentication token. If the current timestamp is within the valid timestamp range, the verification is successful, and the user subject associated with the subject authentication token is allowed to participate in the execution process of cross-platform joint rights and interests. Then, proceed to step S45. Otherwise, the verification fails, and the user subject associated with the subject authentication token is not allowed to participate in the execution process of cross-platform joint rights and interests.

[0132] S45: Generate the rights enforcement rule of the subject authentication token based on the business rule derived key, and send the subject authentication token and the rights enforcement rule of the subject authentication token to the platform associated with the subject authentication token;

[0133] Specifically, the formula for generating the rights enforcement rules for the principal authentication token is as follows:

[0134] ;

[0135] in, This represents the business rule derived key, and D represents the number of rights enforcement rules. , Indicates the subject authentication token The rights enforcement rule number, This represents the modulo operator;

[0136] As an embodiment of the present invention, the rights execution rules include triggering membership level upgrades or rights weighting, enabling enhanced risk control verification and delaying issuance, triggering joint risk control marking (only recording non-execution of rights), triggering joint marketing or subsidy strategies, etc.

[0137] S46: The platform sends business rights to the user entity associated with the entity's identity verification token in accordance with the rights execution rules.

[0138] It should be noted that, through the deterministic mapping between the subject authentication token and the business rule derived key, the rights execution rules of this invention can be stably generated without parsing the user's real identity and cross-platform aggregated data, realizing the business decision of token as rule. This business rule generation mechanism enables the rights issuance, risk control triggering and marketing strategies to have cross-platform consistency and controllable differences, avoids manual configuration and direct data connection, and improves the security, scalability and compliance of joint rights execution.

[0139] Optionally, the main identity verification token is converted into a verification code. The verification code is used to package user rights-related data fragments of the user subject within the platform and transmit them to the platform associated with the main identity verification token. The platform receiving the data packet recalculates the verification code using the main identity verification token held locally and determines whether the recalculated result is consistent with the verification code in the data packet. If they are consistent, it indicates that the user rights-related data fragment is real and reliable data, and the user rights-related data fragment is stored to realize cross-platform data transmission.

[0140] Furthermore, refer to, for example Figure 2 The diagram illustrates the cross-platform rights-based joint data sharing flow. The user entity (user) has multiple rights-related data fragments on both Platform 1 and Platform 2. A blinding processing module blinds the low-dimensional private sketches, forming sketch clusters of the same user entity (user) on different platforms. Then, weighted consistency aggregation and cross-platform aggregation (via a consistency aggregation module) are performed on the double-blinded sketches within these clusters to generate a consistent aggregation result for the user entity (user). This results in the generation of a user authentication token and rights enforcement rules. Simultaneously, each platform is aware of the user entity from which the double-blinded sketches and user rights-related data fragments originate. When transmitting the double-blinded sketches, the consistent aggregation result of the user entity, and the user authentication token, each platform includes a unique user identifier to prevent platforms from failing to identify the user entity corresponding to the rights enforcement rules.

[0141] It should be noted that the terms "comprising," "including," or any other variations thereof used herein are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0143] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A cross-platform equity joint data security sharing method, characterized in that, The method comprises: S1: collecting cross-platform user right-related data segments, performing time decay and private sketch mapping processing on the user right-related data segments based on the data segment generation time stamp corresponding to the user right-related data segments, and obtaining a low-dimensional private sketch with time awareness and user private information protection; S2: performing two rounds of independent blinding processing on the low-dimensional private sketch using platform-side blinding factors and collaborative-side blinding factors to obtain a double-blinded sketch; S3: according to the double-blinded sketch, performing consistency aggregation processing on the double-blinded sketches from the same user subject using a verifiable threshold aggregation protocol to obtain a consistency aggregation result of the same user subject; S4: generating a subject identity verification token of the consistency aggregation result using a business identity generation algorithm based on the consistency aggregation result, and performing cross-platform data security sharing based on the subject identity verification token.

2. The cross-platform rights federation joint data security sharing method of claim 1, wherein, In step S1, the cross-platform user right-related data segments are collected, and the user right-related data segments are subjected to time decay based on the data segment generation time stamp corresponding to the user right-related data segments, comprising: S11: each business platform collects user right-related data segments under the corresponding platform and the data segment generation time stamp corresponding to the user right-related data segments, wherein the user right-related data segments represent data units formed by users at different business platforms and different time periods, which can indirectly reflect the right status or right use behavior of the users; S12: mapping the user right-related data segments into a numerical vector form to obtain a user right-related data segment vector; S13: converting the data segment generation time stamp corresponding to the user right-related data segments into a time decay coefficient; S14: concatenating the time decay coefficient with the corresponding user right-related data segment vector as the time decay processing result of the user right-related data segments.

3. The cross-platform rights federation joint data security sharing method of claim 2, wherein, In step S1, the time decay processing result of the user right-related data segments is subjected to private sketch mapping processing to obtain a low-dimensional private sketch with time awareness and user private information protection, further comprising: S15: performing standardization processing on the time decay processing result of the user right-related data segments by appending 0 at the end to obtain a fixed-length standardized vector; S16: performing irreversible compression mapping on the standardized vector to obtain a fixed-length low-dimensional private sketch.

4. The cross-platform rights federation joint data security sharing method of claim 3, wherein, The compression mapping formula in step S16 is: ； ； ； wherein, denotes a normalized vector, denotes a normalized vector , Len denotes the vector values in the normalized vector, denotes a normalized vector , the vector value in the normalized vector, denotes a normalized vector , the vector value in the normalized vector, , Len denotes the fixed length of the normalized vector, denotes the projection coefficient of the projection vector corresponding to the nth private sketch dimension on the th feature, , N denotes the number of dimensions of the private sketch, s denotes the compression mapping result of the normalized vector , wherein the compression mapping result is in the form of an N-dimensional vector, denotes the compression projection value of the 1th to Nth dimensions in the compression mapping result , respectively, denotes the compression projection value of the nth dimension in the compression mapping result , respectively, denotes a sign function.

5. The cross-platform rights federation joint data security sharing method of claim 1, wherein, In step S2, the low-dimensional private sketch is subjected to two rounds of independent blinding processing using platform-side blinding factors and collaborative-side blinding factors, comprising: S21: constructing platform-side blinding factors and collaborative-side blinding factors, wherein the platform-side blinding factors are in the form of platform-private masks, and the collaborative-side blinding factors are in the form of platform-public offset vectors; S22: performing first-round collaborative blinding processing on the low-dimensional private sketch using the collaborative-side blinding factors to obtain a first-blinded sketch; S23: performing second-round platform blinding processing on the first-blinded sketch using the platform-side blinding factors to obtain a double-blinded sketch.

6. The cross-platform rights federation joint data security sharing method of claim 5, wherein, The generation process of the platform-side blind factor is as follows: S211: constructing a public discriminant index function cluster, the public discriminant index function cluster comprising a plurality of public discriminant index functions consistent with the number of dimensions of the private sketch; S212: extracting platform features and mapping the platform features to platform-side blind factors by using the public discriminant index functions, the length of the platform-side blind factors being consistent with the number of dimensions of the private sketch.

7. The cross-platform rights federation joint data security sharing method of claim 1, wherein, In the S3 step, the consistency aggregation processing of the double-blind sketch from the same user subject is performed by using the verifiable threshold aggregation protocol, comprising: S31: calculating the similarity between different double-blind sketches in the same platform, and dividing the double-blind sketches with a similarity higher than a preset similarity threshold into the same sketch cluster, wherein the double-blind sketches in the same sketch cluster belong to the same user subject under the same platform; S32: performing weighted consistency aggregation processing on the double-blind sketches in the sketch cluster to obtain the aggregated blind sketch of the user subject in the corresponding platform; S33: calculating the consistency of the aggregated blind sketches across platforms, and if the consistency meets the verifiable threshold aggregation protocol, performing cross-platform aggregation on the aggregated blind sketches across platforms to obtain the consistency aggregation result from the same user subject.

8. The cross-platform rights federation joint data security sharing method of claim 1, wherein, In the S4 step, the subject identity authentication token of the consistency aggregation result is generated by using a business identity generation algorithm, comprising: S41: obtaining the effective timestamp range of the subject identity authentication token; S42: extracting all platform business type codes of the platform associated with the consistency aggregation result, and splicing all the platform business type codes to form a platform business type code vector of the consistency aggregation result; S43: splicing the consistency aggregation result, the effective timestamp range, and the platform business type code vector, and mapping the spliced result to a fixed-length subject identity authentication token by using a double mapping function of a fusion hash function and a pseudo-random function.

9. The cross-platform rights federation joint data security sharing method of claim 8, wherein, In the S4 step, the cross-platform data security sharing based on the subject identity authentication token further comprises: S44: sending the subject identity authentication token to the benefit business end, and the benefit business end verifies the effective timestamp range in the subject identity authentication token, if the current timestamp is within the effective timestamp range, it means that the verification is passed, and the user subject associated with the subject identity authentication token is allowed to participate in the execution process of the cross-platform joint benefit, and turning to step S45; otherwise, it means that the verification is not passed, and the user subject associated with the subject identity authentication token is not allowed to participate in the execution process of the cross-platform joint benefit; S45: deriving a key based on a business rule to generate a benefit execution rule of the subject identity authentication token, and sending the subject identity authentication token and the benefit execution rule of the subject identity authentication token to the platform associated with the subject identity authentication token; S46: the platform sends a business benefit to the user subject associated with the subject identity authentication token according to the benefit execution rule.

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