In times of crisis, humanitarian organizations bring aid to those affected (e.g., water, food, medical supplies, cash assistance). Prior works introduced privacy-preserving systems for digitizing the aid distribution process, increasing their efficiency and security. These solutions, by design, do not allow humanitarian organizations to collect metrics about the aid distribution process. Such assessments (e.g., the proportion of aid distributed to a minority) are crucial to enable the organizations to improve their operations, to perform their duty of care, and to enable transparency and accountability towards recipients, donors, and the public in general. In partnership with the International Committee of the Red Cross, we identify assessments relevant to humanitarian aid deployments and these assessments' security and privacy requirements. We introduce a generic framework that augments existing privacy-preserving humanitarian aid distributions with such assessments. This framework enables the collection of aggregate statistics about the aid distribution process without compromising the privacy of recipients, and without requiring any changes to the existing protocols. To realize our framework we introduce one-time functional encryption (1FE), for which we propose efficient realizations from standard cryptographic primitives. We design and implement two variants of our framework: a more efficient one, secure against semi-honest adversaries; and a more robust one, secure against malicious adversaries. We also introduce the novel notions of threat model agility and graceful degradation. These notions enable us to model the unstable environment of humanitarian aid distribution, where the capabilities of the adversary may change suddenly (e.g., when a militia takes over a region in conflict), invalidating the threat model under which the system was originally deployed. We believe these notions are of independent interest for other privacy-preserving applications deployed in unstable environments.

Homomorphic encryption has become a practical solution for protecting the privacy of computations on sensitive data. However, existing homomorphic encryption pipelines do not guarantee the correctness of the computation result in the presence of a malicious adversary. We propose two plaintext encodings compatible with state-of-the-art fully homomorphic encryption schemes that enable practical client-verification of homomorphic computations while supporting all the operations required for modern privacy-preserving analytics. Based on these encodings, we introduce VERITAS, a ready-to-use library for the verification of computations executed over encrypted data. VERITAS is the first library that supports the verification of any homomorphic operation. We demonstrate its practicality for various applications and, in particular, we show that it enables verifiability of homomorphic analytics with less than 3x computation overhead compared to the homomorphic encryption baseline.

Homomorphic encryption has become a promising solution for protecting the privacy of computations on sensitive data. However, existing homomorphic encryption pipelines do not guarantee the correctness of the computation result in the presence of a malicious adversary. In this poster, we present two encodings compatible with state-of-the-art fully homomorphic encryption schemes that enable practical client-verification of homomorphic computations, while enabling all the operations required for modern privacy-preserving analytics. Based on these encodings, we introduce a ready-to-use library for the verification of any homomorphic operation executed over encrypted data. We demonstrate its practicality for various applications and, in particular, we show that it enables verifiability of some homomorphic analytics with less than 3 times overhead compared to the homomorphic encryption baseline.