The SQUEEZES framework accelerates the sharing and collaboration of highly sensitive patient data, enabled by emerging technologies for Privacy Enhanced Computations (PECs) and Privacy Preserving Record Linkage (PPRL). This framework utilizes PECs to expedite collaborative data access and enables researchers to build higher quality models from richer aggregated data linked and joined with PPRL from multiple sources for centralized data analysis and management. SQUEEZES is particularly vital when it is difficult or impossible for data owners to share their data due to privacy concerns, allowing them to retain control over how their data is used. It is a standards-compliant framework designed to: (1) reduce the timeline of sharing data from months to hours while lowering financial and administrative barriers for data sharing, (2) automate the sharing and linking of privacy-protected data used in analytics workflows, reducing the workload of data alignment and schema matching, (3) support existing cancer research data workflows, (4) allow data owners to control the use of their data even after it is shared, protecting patients and data owners, including historically underserved communities, (5) create an incentivization framework for data owners to maintain control over the results of computations on their data, and (6) be scalable, extensible, and easily deployable on legacy and emerging standards-compliant systems.