The rise of data-driven systems has created a fundamental tension for institutions. Banks, healthcare providers, governments, and large enterprises increasingly depend on analyzing sensitive data to operate effectively, yet they are simultaneously constrained by strict privacy requirements and regulatory obligations. This tension has traditionally forced institutions into a difficult tradeoff between utility and confidentiality.
On one hand, institutions need to extract value from data. Financial institutions rely on transaction data to detect fraud and assess risk. Healthcare organizations depend on patient data to improve diagnostics and advance research. Governments analyze population-level data to inform policy decisions and allocate resources. In each case, the ability to compute on large, diverse datasets directly impacts performance, competitiveness, and public outcomes.
On the other hand, these same datasets are highly sensitive. Regulatory frameworks such as GDPR and HIPAA im

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