All news with #data de-identification tag

Amazon Connect adds in-flight chat redaction and processing

🔒 Amazon Connect now intercepts chat messages before delivery to enable automatic sensitive-data redaction and custom message processing. The built-in redaction detects entities such as credit card and social security numbers across multiple language variants and can replace them with generic or entity-specific placeholders (e.g., [PII] or [NAME]). Businesses can also integrate custom processors for translation, profanity filtering, or other transformations to meet compliance and CX needs. The feature is available in multiple AWS regions.

AWS Clean Rooms Adds Synthetic Dataset Generation for ML

🔒 AWS now enables AWS Clean Rooms to generate privacy-enhancing synthetic datasets for training regression and classification ML models without exposing raw records. The capability de-identifies subjects in the original data and reduces the risk of models memorizing sensitive information, allowing partners to collaborate on model training while preserving privacy. Typical use cases include campaign optimization, fraud detection, and medical research.

Amazon Aurora adds PostgreSQL minor versions and DDM support

🔒 Amazon Aurora PostgreSQL-Compatible Edition now supports PostgreSQL 17.6, 16.10, 15.14, 14.19, and 13.22, bringing community fixes plus Aurora-specific enhancements. The release introduces database-level Dynamic Data Masking (DDM) for 16.10 and 17.6 to mask sensitive column values at query time using role-based policies without altering stored data. Additional updates include a shared plan cache, improved performance and recovery-time-objective (RTO), and more reliable Global Database switchovers. New clusters can be created in the Amazon RDS console or existing databases upgraded; releases are available across all commercial AWS Regions and AWS GovCloud (US).

Amazon Aurora PostgreSQL Adds Dynamic Data Masking

🔒 Amazon Aurora PostgreSQL-Compatible Edition now supports dynamic data masking using the new pg_columnmask extension, enabling column-level protection at query time. The extension complements PostgreSQL row-level security and column grants by letting administrators define SQL-based masking policies that alter how data appears to users without changing stored values. Policies can use built-in or user-defined functions to hide, partially mask, or transform data, and multiple policies can be applied with weighted precedence. pg_columnmask protects results across WHERE, JOIN, ORDER BY, and GROUP BY clauses and is available for Aurora PostgreSQL 16.10+ and 17.6+ in all regions.

How to Scrub and Minimize Your Digital Footprint Effectively

🔍 Regularly search for yourself—names, emails and usernames—to uncover forgotten accounts, impersonators, and exposed data. Delete obsolete accounts, revoke third‑party access, clear browser and device traces, and use unique passwords stored in a reliable manager. Use tools like Just Delete Me and breach monitors such as Have I Been Pwned, invoke your right to be forgotten where applicable, and request archive removals. Tighten app permissions, unsubscribe from old lists, and consider privacy‑focused services or stronger 'paranoid' measures if needed.

Amazon Connect Contact Lens Adds Redaction in 7 Languages

🔒 Amazon Connect Contact Lens now provides automatic sensitive data redaction for voice and chat conversational analytics in French (France, Canada), Portuguese (Portugal, Brazil), Italian, German, and Spanish (Spain). You can remove PII, financial account numbers and PINs, and Internet access details from transcripts and audio files, choosing to redact selected entities or all detected sensitive data. Redacted values can be replaced with a generic placeholder (e.g., [PII]) or an entity-specific placeholder (e.g., [NAME]). Sensitive data redaction is available in all AWS Regions where Amazon Connect is offered.

AWS Clean Rooms ML adds redacted error log summaries

🔒 AWS Clean Rooms ML collaborators can now configure a privacy control to send redacted error log summaries to selected collaboration members. Summaries include exception type, error message, and the line in the code where the error occurred. When associating a model with a collaboration, parties decide which members receive summaries and whether detectable PII, numbers, or custom strings will be redacted. This helps teams debug models while protecting sensitive data and intellectual property.

EuroDaT and Google Cloud: Secure Financial Data Exchange

🔒 EuroDaT, a state-owned data trustee, built safeAML with major German banks to enable controlled, pseudonymous transaction matching while preserving GDPR compliance. The cloud-native service runs on Google Cloud and Google Kubernetes Engine, using infrastructure-as-code, isolated VPCs and auditable processing so EuroDaT never accesses personal-data content. By letting banks request targeted supplementary information, safeAML accelerates suspicious-activity checks, reduces false positives and lays groundwork for wider use in ESG and health data sharing.

Google rolls out age assurance to protect U.S. youth

🛡️ Over the coming weeks Google will begin a limited U.S. rollout of age assurance, a system designed to distinguish users under 18 from adults and apply age-appropriate protections across its products. For accounts identified as minors Google will enable defaults such as YouTube Digital Wellbeing tools, disable Maps Timeline, turn off personalized advertising, and block adult-only apps on Google Play. The approach combines machine-learning age estimation based on existing account signals with optional age verification — including a government ID or a selfie — when users dispute their estimated age, and Google will notify users and provide options for adult verification.

Secure Age Assurance for Europe and Global Internet

🔒 Google outlines a privacy-forward approach to online age assurance that emphasizes interoperability and targeted protections for children, teens, and parents. The post highlights the new Credential Manager API on Android, which enables sites and apps to request only necessary age information from trusted credential holders. Backed by zero-knowledge proofs, the system can verify age thresholds (for example, over 18) without exposing identity or additional personal data. Google urges standards development and cross-sector collaboration to extend and adopt this secure infrastructure.