Projects
PAIDIT: Private Anonymous Identity for Digital Transfers
Partner: ICRC, funded by HAC Partner contact: TBD EPFL laboratory: Decentralized Distributed Systems Laboratory (DEDIS) EPFL contact: Prof. Bryan Ford To serve the 80 million forcibly-displaced people around the globe, direct cash assistance is gaining acceptance. ICRC’s beneficiaries often do not have, or do not want, the ATM cards or mobile wallets normally used to spend or withdraw cash digitally, because issuers would subject them to privacy-invasive identity verification and potential screening against sanctions and counterterrorism watchlists. On top of that, existing solutions increase the risk of data leaks or surveillance induced by the many third parties having access to the data generated in the transactions. The proposed research focuses on the identity, account, and wallet management challenges in the design of a humanitarian cryptocurrency or token intended to address the above problems.
Topics • Privacy Protection & Cryptography • Blockchains & Smart Contracts • Device & System Security • Finance • Government & Humanitarian
ViewHarmful Information Against Humanitarian Organizations
Partner: ICRC, funded by HAC Partner contact: Fabrice Lauper EPFL laboratory: Distributed Information Systems Laboratory (LSIR) EPFL contact: Prof. Karl Aberer, Rebekah Overdorf In this project, we are working with the ICRC to develop technical methods to combat social media-based attacks against humanitarian organizations. We are uncovering how the phenomenon of weaponizing information impacts humanitarian organizations and developing methods to detect and prevent such attacks, primarily via natural language processing and machine learning methods.
Topics • Machine Learning • Government & Humanitarian
ViewAdversarial Attacks in Natural Language Processing Systems
Partner: Cyber-Defence Campus (armasuisse) Partner contact: Ljiljana Dolamic EPFL laboratory: Signal Processing Laboratory (LTS4) EPFL contact: Prof. Pascal Frossard, Sahar Sadrizadeh Recently, deep neural networks have been applied in many different domains due to their significant performance. However, it has been shown that these models are highly vulnerable to adversarial examples. Adversarial examples are slightly different from the original input but can mislead the target model to generate wrong outputs. Various methods have been proposed to craft these examples in image data. However, these methods are not readily applicable to Natural Language Processing (NLP). In this project, we aim to propose methods to generate adversarial examples for NLP models such as neural machine translation models in different languages. Moreover, through adversarial attacks, we mean to analyze the vulnerability and interpretability of these models.
Topics • Device & System Security • Machine Learning • Government & Humanitarian
ViewPriBAD: Private Biometrics for Aid Distribution
Partner: ICRC, funded by HAC Partner contact: Vincent Graf EPFL laboratory: Security and Privacy Engineering Laboratory (SPRING) EPFL contact: Prof. Carmela Troncoso, Wouter Lueks In this project, we work on providing a privacy-preserving biometric solution for humanitarian aid distribution. The project seeks to understand the requirements of aid distribution in emergency situation and design a solution that enables the use of biometrics without endangering the beneficiaries that need access to aid.
Topics • Privacy Protection & Cryptography • Government & Humanitarian
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