XAFE PRIVACY ENHANCING TECHNOLOGIES
Xafe's platform offers a complete range of privacy-enhancing technologies. Anonymization (Masking, Pseudonymization, Tokenization) secures identities, while Obfuscation (k-Anonymity, l-Diversity, t-Closeness) prevents re-identification. Privatization applies Differential Privacy (Laplace, Gaussian, Voronoi) for controlled noise, safeguarding data utility. Confidential Computing uses Homomorphic Encryption for secure operations on encrypted data. Lastly, Generalization (Recoding, Binning, Aggregation) ensures data abstraction and compliance. This integrated approach delivers robust protection and privacy for diverse data needs.
ANONYMIZATION
Masking & Suppression: This technique hides specific data fields, like masking credit card numbers, where only the last four digits are visible (e.g., 1234-XXXX-XXXX-5678). It’s useful for call centers needing partial visibility for customer verification.
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Hashing: Uses cryptographic hashing (e.g., SHA-256) to convert sensitive information (like user emails) into fixed-length, irreversible hash codes, ensuring no direct link to original values.
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Pseudonymization: Replaces direct identifiers (e.g., names) with pseudonyms (e.g., user ID codes), which can be mapped back only with secure access to a separate decryption key or reference table.
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Tokenization: Replaces sensitive data with a token (like a randomly generated number). For example, substituting a credit card number with a token in payment systems, so actual card data remains secure.
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OBFUSCATION
k-Anonymity: Ensures that any individual is indistinguishable from at least k−1 others in the dataset. For example, generalizing ages to age ranges (e.g., 30-40 years) when sharing healthcare data, ensuring at least kk individuals share each trait.
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l-Diversity: Ensures diversity in sensitive attributes within each group. For example, if age groups are created, each should have a range of income values to avoid re-identification based on income patterns.
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t-closeness: Enhances l-Diversity by ensuring the distribution of sensitive data within each group is close to the overall dataset’s distribution. For instance, for a dataset with income data, each group will have an income distribution similar to the overall data, making re-identification harder.
PRIVATIZATION
Differential Privacy: Adds controlled noise to data to prevent re-identification with following:
Laplace Mechanism: Adds noise from a Laplace distribution, useful in releasing summaries like averages without revealing individual values. For example, reporting average income with slight noise.
Gaussian Mechanism: Adds Gaussian distributed noise, often for complex datasets requiring more rigorous protection.
Voronoi Noise: Uses a geometric approach, protecting data by grouping points within Voronoi cells, which can enhance spatial privacy for data like geographic coordinates.
CONFIDENTIAL COMPUTING
Partial Homomorphic Encryption: Allows certain operations on encrypted data, like addition. Useful for encrypted voting systems, where votes are counted without revealing individual choices.
Somewhat Homomorphic Encryption: Allows limited operations (e.g., certain additions and multiplications) on encrypted data. It could be used in secure data aggregation for outsourced analytics where partial data processing is needed.
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Fully Homomorphic Encryption: Supports arbitrary operations on encrypted data, allowing complete computation without decryption. For example, encrypted medical data can be analyzed without ever decrypting it, preserving patient privacy.
GENERALIZATION
Hierarchical & Global Recoding: Transforms data into broader categories using a predefined hierarchy (e.g., replacing exact ages with age groups like "20-30"). This reduces data granularity, enhancing privacy while retaining meaningful information.
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Binning & Aggregation: Groups numeric values into bins (e.g., income ranges) or aggregates data points to create summarized categories, limiting the precision of individual records and reducing re-identification risks.
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Micro-Aggregation & Rounding: Clusters similar data points and replaces them with their average (micro-aggregation) or approximates values to the nearest round number. This minimizes individual-level details while preserving overall data trends.
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Top/Bottom Coding: Caps extreme values at predefined limits (e.g., incomes above a certain threshold are recorded as "Above $200K"), protecting privacy for outliers while maintaining the dataset’s usability for analysis.
