Privacy-Preserving Computation for Nuclear Safeguards

Nuclear safeguards are a key component in international efforts to prevent the proliferation of nuclear weapons. The International Atomic Energy Agency (IAEA) and its nuclear inspectors are tasked with administering these safeguards measures, ensuring to the best of their ability that weaponizable nuclear material is not created or diverted without the knowledge of the international community. However, no safeguards system is invincible, and so it is necessary for the IAEA to be constantly advancing and expanding its capabilities. At the same time, despite the vast majority of the international community agreeing that the proliferation of nuclear weapons should be avoided via their commitments permitting the IAEA to administer safeguards within their jurisdictions, it is expected that some States will be hesitant to allow the agency to significantly expand the scope of implemented safeguards. This leaves large quantities of potentially insightful data unavailable to safeguards administrators evaluating compliance with international regulations.This dissertation proposes a solution to reconcile these competing interests in the form of privacy- preserving computation. Using privacy-preserving algorithms, IAEA inspectors and IAEA Mem- ber States may realize a new paradigm where nuclear facility data can be analyzed for safeguards purposes without that data ever being revealed to anyone other than the facility operators. These algorithms open the possibility that entirely new data streams may become accessible to IAEA inspectors, especially those that lend themselves to modern data analytics techniques.This work represents the culmination of recent efforts to showcase for the first time how nuclear safeguards could be implemented in a privacy-preserving context. It includes a proof-of-concept demonstration of how the privacy-preserving technique of garbled circuits could be used for a safe- guards analysis of real nuclear data, highlighting the present feasibility of such algorithms. It also introduces CypherCircuit, a new software framework for building and evaluating garbled circuits that is intended to facilitate the adoption of privacy-preserving technologies by skeptical or non- expert audiences. The CypherCircuit Python package is designed to cultivate an understanding of garbled circuits through an intuitive, transparent, and accessible design, encouraging the application of privacy-preserving techniques to novel challenges—in this case nuclear safeguards