WiMi Developed an Efficient Density-Peak Clustering Algorithm for Improving Policy Evaluation Performance

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that it developed DPCEngine, an efficient density-peak clustering algorithm for improving the performance of policy evaluation. It reduces the complexity of policy evaluation by identifying the clustering structure in the policy sets. The structure and algorithmic process of WiMi’s DPCEngine, which includes key steps such as data pre-processing, density-peak clustering, strategy matching and evaluation.

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To evaluate the performance and effectiveness of DPCEngine, experiments were conducted using a real dataset containing a large and complex set of policies. This dataset contains policies from different domains and covers a wide range of access control scenarios. This dataset is divided into a training set and a test set, where the training set is used to build the model of DPCEngine and the test set is used to evaluate its performance.

WiMi’s researchers compared DPCEngine with those traditional policy evaluation methods, including linear search-based and tree structure-based methods. Two aspects of performance metrics were evaluated: policy evaluation time and matching accuracy. Policy evaluation time is the time required to evaluate an access request, while matching accuracy is the consistency between DPCEngine’s matching results and traditional methods.

DPCEngine offers significant performance advantages in terms of policy evaluation time. Compared to traditional methods, DPCEngine is able to significantly reduce the policy evaluation time, especially when the policy set is large and complex. This is attributed to the density-peak-based clustering algorithm used by DPCEngine, which is able to cluster the policy set into smaller subsets, thus reducing the search space for evaluation.

The experimental results of WiMi’s DPCEngine in terms of matching accuracy show that there is a high degree of consistency between DPCEngine’s matching results and traditional methods. This indicates that DPCEngine does not sacrifice accuracy while improving the performance of strategy evaluation. In addition, we conducted scalability experiments to evaluate the performance of DPCEngine under different sizes of policy sets. The results show that DPCEngine can effectively cope with large-scale policy sets and has good scalability.

WiMi’s DPCEngine, a policy evaluation engine based on a density-peak clustering algorithm, has three main functions: preprocessing policy sets, clustered policy sets, and matching policies. The combined use of these functions can significantly improve the effectiveness and accuracy of strategy evaluation.

Preprocessing the policy sets: before strategy evaluation, DPCEngine prepares the data by preprocessing the policy set to make it more suitable for density peak clustering. The preprocessing process includes steps such as data cleaning, feature extraction and data transformation. By cleaning the data, redundant, incomplete or incorrect strategy information is removed to ensure the accuracy and consistency of the data. Avoid negative impact on the evaluation results. The feature extraction process, on the other hand, extracts key features from the policy set, such as user roles, resource types, and operation privileges, for subsequent clustering operations. Data transformation converts the policy set into a data representation, such as a vector or matrix, suitable for density-peaked clustering algorithms for clustering analysis.

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Clustered policy sets: DPCEngine utilizes the DPC algorithm to perform clustering operations on policy sets. The Density Peak Clustering Algorithm(DPCA) identifies the clustering structure in a set of strategies by evaluating the density and distance between strategies. The algorithm identifies peak points based on the density and distance between strategies and divides the strategies between peak points into different clusters. This reduces the time and complexity of policy evaluation by clustering a large and complex set of policies into smaller subsets, where each cluster represents a set of policies with similar characteristics and behavioral patterns. The result of a clustered policy set is a set of policy clusters with similar characteristics and behavioral patterns, and this clustered policy set approach reduces the time and computational complexity of policy evaluation and improves the performance and efficiency of the system.

Matching policies: DPCEngine utilizes the clustering results for policy matching. When an access request arrives, DPCEngine compares and matches it with pre-generated policy clusters. By searching for the most similar policies in each cluster, DPCEngine is able to quickly determine the set of policies that match the access request. This clustering-based matching approach can significantly speed up policy matching and provide accurate matching results. In addition, DPCEngine can be combined with other access control technologies and rules engines to further optimize the policy matching process and ensure system security and compliance.

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