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Eng."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Secure multi-party computation (MPC) enables privacy-preserving  \ncomputations using secret data, with applications ranging from health  \ncare and finance to machine learning and blockchains. MPC compilers  \ntranslate high-level function descriptions to the low-level  \nrepresentations required for the actual execution, making them  \ncritical for both usability and scalability of MPC. However, these  \ncompilers may contain logic bugs that cause them to quietly produce  \nwrong outputs, the consequences of which could be catastrophic given  \nthe sensitive applications of this technology. Testing MPC compilers  \nin order to find these severe bugs is, therefore, paramount.<\/jats:p>\n                  <jats:p>With only a single testing tool currently available (which is not  \npublicly available in its entirety and has several serious  \nlimitations), this issue is far from resolved. In this paper, we  \npresent BabelFuzz, a cost-effective framework for testing MPC compilers.  \nBy introducing an expressive intermediate representation (IR) for its  \nseed-program generation, BabelFuzz is able to support multiple compilers  \nthat use different input languages, while keeping the development  \neffort of adding new targets low. Even better, this approach allows us  \nto translate our IR to mainstream languages, which provides a powerful  \ndifferential-testing oracle for highly efficient bug detection.<\/jats:p>\n                  <jats:p>BabelFuzz not only found 27 new logic bugs across four MPC compilers, but  \nit is also able to rediscover every fixed bug the previous state of  \nthe art in testing MPC compilers found.<\/jats:p>","DOI":"10.1145\/3808206","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:06:14Z","timestamp":1782839174000},"page":"4529-4550","source":"Crossref","is-referenced-by-count":0,"title":["Cost-Effective Testing of MPC Compilers"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5108-6410","authenticated-orcid":false,"given":"Sebastian","family":"Watzinger","sequence":"first","affiliation":[{"name":"TU Wien, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1496-1104","authenticated-orcid":false,"given":"Valentin","family":"W\u00fcstholz","sequence":"additional","affiliation":[{"name":"Diligence Security, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0888-3093","authenticated-orcid":false,"given":"Deepak","family":"Garg","sequence":"additional","affiliation":[{"name":"MPI-SWS, Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2649-1958","authenticated-orcid":false,"given":"Maria","family":"Christakis","sequence":"additional","affiliation":[{"name":"TU Wien, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Athos. https:\/\/github.com\/mpc-msri\/EzPC\/tree\/master\/Athos."},{"key":"e_1_2_1_2_1","unstructured":"The EMP Toolkit. https:\/\/github.com\/emp-toolkit."},{"key":"e_1_2_1_3_1","unstructured":"n. d.]. EzPC: Easy Secure Multiparty Computation. https:\/\/github.com\/mpc-msri\/EzPC."},{"key":"e_1_2_1_4_1","unstructured":"n. d.]. MP-SPDZ: Versatile Framework for Multi-Party Computation. https:\/\/github.com\/data61\/MP-SPDZ."},{"key":"e_1_2_1_5_1","unstructured":"MT-MPC: Metamorphic Testing of Secure Multi-Party Computation (MPC) Compilers. https:\/\/github.com\/ winnylyc\/MT-MPC."},{"key":"e_1_2_1_6_1","unstructured":"n. d.]. Silph: A Framework for Scalable and Accurate Generation of Hybrid MPC Protocols. https:\/\/github.com\/circify\/ circ\/tree\/mpc_aws."},{"key":"e_1_2_1_7_1","unstructured":"n. d.]. SIRNN: Secure Inference for Recurrent Neural Networks. https:\/\/github.com\/mpc-msri\/EzPC\/tree\/master\/ SIRNN."},{"key":"e_1_2_1_8_1","first-page":"1","article-title":"Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation","volume":"63","author":"Benhamouda Fabrice","year":"2019","unstructured":"Fabrice Benhamouda, Shai Halevi, and Tzipora Halevi. 2019. 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