Honeypot Data Generation via Differential Privacy
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Solution Overview
Problem
Honeypots require realistic fake data to effectively deceive attackers, but existing methods struggle to generate believable and coherent fake data while protecting sensitive information.
Innovation Solution
The use of differential privacy algorithms, such as generative adversarial networks (GANs), to modify and generate fake data that maintains the original data distribution while ensuring privacy, by splitting and rebuilding complex fields, removing duplicates, and using noise injection to create believable and consistent fake data for honeypot seeding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real user data is used in honeypots to make them appear authentic, then attackers are more likely to interact with the honeypot, but sensitive information may be exposed if the honeypot is compromised
Solution Approach 1:
The patent creates synthetic copies of real user data that replicate the statistical properties, distributions, and relationships of authentic data without containing actual sensitive information. These synthetic copies are injected into honeypots to make them appear realistic to attackers while eliminating the risk of exposing real user data.
Solution Approach 2:
The system modifies data parameters by transforming real data into synthetic data with adjusted statistical characteristics. Through differential privacy techniques and generative models, the data undergoes parameter transformations that preserve overall patterns while removing identifiable information, enabling honeypot deployment without sensitivity risks.
2Ease of manufacture
If synthetic data is generated for honeypots without using differential privacy, then data generation is simpler, but the fake data may not adequately protect sensitive information patterns
Solution Approach 1:
The system implements feedback loops where generated synthetic data is continuously evaluated against privacy metrics and data utility requirements. Differential privacy mechanisms provide feedback by adding controlled noise to ensure privacy guarantees, while generative adversarial networks use discriminator feedback to improve the realism of synthetic data over iterative training cycles.
Solution Approach 2:
The patent introduces differential privacy mechanisms and generative models as intermediary layers between real sensitive data and the honeypot deployment. These intermediaries transform the data through multiple processing stages, including noise addition, feature transformation, and synthetic generation, ensuring privacy protection while maintaining data utility.
3Stability of the object's composition
If complex fields are not split during data processing, then the data structure remains intact, but duplicate fields and inconsistencies may reduce data quality
Solution Approach 1:
The patent segments complex data fields into smaller, manageable components during the synthetic data generation process. By splitting complex fields, the system can process and generate each component independently, ensuring consistency and eliminating duplicates while maintaining the overall structure through controlled reassembly of the segmented components.
Data Source
AI summary
Data is received that includes a plurality of fields. These fields are modified using at least one differential privacy algorithm to result in fake data. This fake data is subsequently used to seed and enable a honeypot so that access to such honeypot and fake data can be monitored and/or logged. Related apparatus, systems, techniques and articles are also described.