Privacy-Preserving Text Redaction via MadLib Semantic Replacement
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Solution Overview
Problem
Common data privacy solutions such as regex pattern matching, ad-hoc filters, and anonymization strategies are ineffective in preventing privacy breaches, as they cannot anticipate or account for potential side knowledge an attacker may use with a released dataset, particularly in handling utterance data that contains sensitive information.
Innovation Solution
Implementing a differential privacy-compliant redaction algorithm using MadLib-style replacements and metric differential privacy to generate redacted utterance data, which splits text into public and private portions and replaces sensitive information with semantically similar alternatives, ensuring privacy preservation and intent preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If common data privacy solutions such as regex pattern matching, ad-hoc filters, and anonymization strategies are used, then data processing is simple and fast, but they are provably non-private and cannot prevent privacy breaches when attackers use side knowledge
Solution Approach 1:
The patent applies preliminary action by performing differential privacy mechanisms on the training data before it is used to train machine learning models. This pre-processing step adds controlled noise to the data, which prevents privacy breaches during model training while maintaining the utility of the data for learning. The noise is added in advance, before any potential privacy threats occur during the modeling process.
Solution Approach 2:
The patent introduces differential privacy as an intermediary mechanism between the raw private data and the machine learning model. This intermediary layer processes the data through controlled noise addition and perturbation, acting as a buffer that protects the original private information while still allowing the model to learn useful patterns. The intermediary transformation ensures that no direct access to sensitive data occurs during training.
2Reliability
If sensitive information is completely removed from utterance data, then privacy is protected, but the original intent and meaning of the data is lost
Solution Approach 1:
The patent applies local quality by selectively applying different levels of privacy protection to different parts of the data. Rather than uniformly removing or obfuscating all sensitive information, the system applies targeted differential privacy mechanisms that preserve the semantic meaning and intent of utterances while protecting specific sensitive elements. This allows the data to maintain its useful characteristics in non-sensitive areas while providing enhanced protection where needed.
Solution Approach 2:
The patent uses parameter changes by adjusting the privacy budget and noise levels in the differential privacy mechanism to balance privacy protection with information preservation. By carefully tuning the epsilon parameter and other control variables, the system can achieve different levels of privacy utility trade-offs, ensuring that the redacted data remains useful for its intended purposes while providing adequate privacy protection.
3Reliability
If differential privacy mechanisms are applied to protect sensitive information, then privacy is preserved against side knowledge attacks, but the data utility and analytical value may be reduced
Solution Approach 1:
The patent applies partial action by selectively applying differential privacy mechanisms only to the portions of data that require protection, rather than uniformly processing all data. This allows the system to maintain high utility for non-sensitive data while providing targeted privacy protection where needed. The selective application minimizes the overall impact on data utility while still achieving the necessary privacy guarantees for sensitive information.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for providing privacy and intent preserving redactions of text derived from utterance data. Certain embodiments provide new techniques for using MadLib-style replacements to replace one or more terms or phrases in a text string. Example methods may include receiving utterance data and determining a public portion and a private portion of the utterance data. Certain methods include determining a cluster of candidates having a same semantic context as the private portion and identifying from within the cluster of candidates a first candidate. Certain methods include determining a redacted utterance comprising the public portion of the utterance and the first candidate. Certain methods include providing the redacted utterance to downstream systems and processes.