Privacy-Centric AI Framework, Bangalore
Budget: £10 – £15 GBP
I’m setting up an ethical AI initiative in Bangalore that will process personal data from users of varied ethnic backgrounds—Caucasian, Chinese, South-East Asian, African, Arabian, Latino and Indian. Our highest priority is safeguarding privacy and data protection while staying fully aligned with current local data-protection laws.
To move from concept to a working framework, I need practical support designing and documenting a privacy-by-design architecture. The scope covers data intake through to model deployment, ensuring that no personally identifiable information slips through any stage of the pipeline.
Deliverables
• Complete end-to-end data-flow map highlighting risk points
• Consent, retention and deletion policies compliant with Indian statutes
• Anonymisation / pseudonymisation routines (Python or equivalent) integrated into our existing MLOps setup (Azure + Kubeflow)
• Transparent logging and role-based access-control matrix ready for audit
• Developer guidelines and a compliance checklist verifiable by an internal privacy impact assessment
Acceptance criteria: the framework must pass our internal PIA, and a test model trained under it must demonstrate zero exposure of personal identifiers.
Hands-on experience with differential privacy, secure ML pipelines and local Indian data-protection requirements will be invaluable. Let’s build a privacy-centric AI system we can all stand behind.
To move from concept to a working framework, I need practical support designing and documenting a privacy-by-design architecture. The scope covers data intake through to model deployment, ensuring that no personally identifiable information slips through any stage of the pipeline.
Deliverables
• Complete end-to-end data-flow map highlighting risk points
• Consent, retention and deletion policies compliant with Indian statutes
• Anonymisation / pseudonymisation routines (Python or equivalent) integrated into our existing MLOps setup (Azure + Kubeflow)
• Transparent logging and role-based access-control matrix ready for audit
• Developer guidelines and a compliance checklist verifiable by an internal privacy impact assessment
Acceptance criteria: the framework must pass our internal PIA, and a test model trained under it must demonstrate zero exposure of personal identifiers.
Hands-on experience with differential privacy, secure ML pipelines and local Indian data-protection requirements will be invaluable. Let’s build a privacy-centric AI system we can all stand behind.