Purdue University Graduate School
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Language-Based Techniques for Policy-Agnostic Oblivious Computation

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posted on 2024-04-28, 00:32 authored by Qianchuan YeQianchuan Ye

Protecting personal information is growing increasingly important to the general public, to the point that major tech companies now advertise the privacy features of their products. Despite this, it remains challenging to implement applications that do not leak private information either directly or indirectly, through timing behavior, memory access patterns, or control flow side channels. Existing security and cryptographic techniques such as secure multiparty computation (MPC) provide solutions to privacy-preserving computation, but they can be difficult to use for non-experts and even experts.

This dissertation develops the design, theory and implementation of various language-based techniques that help programmers write privacy-critical applications under a strong threat model. The proposed languages support private structured data, such as trees, that may hide their structural information and complex policies that go beyond whether a particular field of a record is private. More crucially, the approaches described in this dissertation decouple privacy and programmatic concerns, allowing programmers to implement privacy-preserving applications modularly, i.e., to independently develop application logic and independently update and audit privacy policies. Secure-by-construction applications are derived automatically by combining a standard program with a separately specified security policy.


Degree Type

  • Doctor of Philosophy


  • Computer Science

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Benjamin Delaware

Additional Committee Member 2

Roopsha Samanta

Additional Committee Member 3

Milind Kulkarni

Additional Committee Member 4

David Darais

Additional Committee Member 5

Tianyi Zhang