Data anonymization
services
The data anonymization techniques we use
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Contextual replacement
We generate realistic, context-aware substitutes for sensitive data using machine learning models, efficient heuristics and offline SLMs that preserve the original format and meaning of the information. This technique ensures anonymized data maintains its business value while protecting individual privacy through replacements that fit naturally within the sample or query.
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Pseudonymization
With this method, we substitute direct identifiers with pseudonyms or artificial identifiers. The data can be re-identified when needed through secure key management. This approach provides privacy protection while maintaining data relationships and allowing authorized access to the original information for legitimate purposes.
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Format-preserving randomization
By using format-preserving randomization, you can maintain the original data format while making the information unreadable and unrecoverable. This technique is essential for protecting structured data, such as credit card numbers, phone numbers, and identification codes, while preserving their functional characteristics for seamless processing by external services.
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Synthetic data generation
Synthetic data generation produces completely artificial datasets that statistically resemble the original data without including any real personal information. This method eliminates privacy risks while providing realistic test data for development, analytics, and training machine learning models.
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Data perturbation
We modify original data values by adding controlled noise or slight variations. This way we can preserve statistical properties while protecting individual privacy. This technique is particularly relevant for numerical data, where maintaining distribution patterns is crucial for accurate analysis.
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Removal and suppression
We selectively remove or obscure specific data fields that pose privacy risks while preserving the utility of the remaining information. This technique is ideal for scenarios where it is acceptable to eliminate certain sensitive elements without affecting the data's analytical value.
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Data masking
The data masking method replaces sensitive data with fictional yet realistic characters, symbols, or values with tags like "[Date]", preserving the original data structure. This technique helps protect personally identifiable information while allowing the system to function normally.
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Data swapping
With the data swapping technique, we rearrange attribute values between different records to break the link between individuals and their sensitive information. This approach maintains the overall statistical distribution of the dataset while making it impossible to identify specific individuals through their unique data combinations.
We start by analyzing your data to find all sensitive information and identify its usage (requests, daily activities) across your systems, databases, and file repositories. Our data engineering specialists and security experts assess privacy risks, regulatory requirements, and business objectives to create a detailed list of data elements that need protection.
Based on our discovery findings, our team selects the optimal combination of anonymization techniques, considering factors like data types, compliance requirements, intended analytical purposes as well as the expected performance of the LLM or AI agent. We focus on balancing privacy protection with data utility for your specific use cases.
At this stage, we integrate our offline data anonymization solution and apply chosen techniques to your sensitive information. Our process involves thorough testing, quality assurance, and validation to confirm that the anonymized data satisfies security standards and business needs.
After receiving responses from AI agents or external services, our decoder component automatically restores the original data context while preserving the valuable insights or outputs generated. This ensures you receive meaningful, actionable results that align with your original data structure and business requirements.
We offer ongoing monitoring of your anonymization processes to ensure consistent protection and optimal performance as your data environment shifts. Our team conducts regular audits, optimizes techniques, updates compliance measures, and provides technical support to protect sensitive data and keep your anonymization solution effective.
Our clients benefit from decades of experience in data science, machine learning development, MLOPs, LLMOps, and cybersecurity, all of which are applied to every anonymization project. Our experts understand the complexities of data protection and how data needs to be anonymized for AI, so it maintains its utility for machine learning model training and testing.
ELEKS delivers end-to-end privacy solutions that combine security and flexibility. Our holistic approach to data anonymization ensures seamless integration across your entire data ecosystem, enabling you to accelerate compliance while maintaining the analytical value of your data.
We tailor our data anonymization techniques to meet your industry-specific needs and regulatory requirements. This personalized approach ensures that our anonymization methods address your specific data privacy challenges.
