Towards a block-cipher circuit resistant to multiple side channel attacks. This project aims to design, implement and verify the first ever fully comprehensive countermeasure against simultaneous attacks on devices performing a cryptographic task, such as a smart card or a mobile phone used for an electronic payment, through a variety of side channels. Side channel attacks aim to obtain unauthorized access to secret cryptographic keys used by such devices, by collecting and statistically process ....Towards a block-cipher circuit resistant to multiple side channel attacks. This project aims to design, implement and verify the first ever fully comprehensive countermeasure against simultaneous attacks on devices performing a cryptographic task, such as a smart card or a mobile phone used for an electronic payment, through a variety of side channels. Side channel attacks aim to obtain unauthorized access to secret cryptographic keys used by such devices, by collecting and statistically processing measurements of by-products of the physical operation of these devices, such as the consumed power or electromagnetic radiation. Rapid proliferation of personal devices used for electronic payments or containing sensitive data makes such a comprehensive countermeasure urgently needed.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100584
Funder
Australian Research Council
Funding Amount
$370,000.00
Summary
Secure and Private Machine Learning. This project intends to answer the question: How can machines learn from data when participants behave maliciously for personal gain? Machine learning and statistics are used in many technologies where participants have an incentive to game the system (eg internet ad placement, e-commerce rating systems, credit risk in finance, health analytics and smart utility grids). However, little is known about how well state-of-the-art statistical inference techniques ....Secure and Private Machine Learning. This project intends to answer the question: How can machines learn from data when participants behave maliciously for personal gain? Machine learning and statistics are used in many technologies where participants have an incentive to game the system (eg internet ad placement, e-commerce rating systems, credit risk in finance, health analytics and smart utility grids). However, little is known about how well state-of-the-art statistical inference techniques fare when data is manipulated by a malicious participant. The project's outcomes aim to ensure that statistical analysis is accurate while preserving data privacy, providing theoretical foundations of secure machine learning in adversarial domains. Potential applications range from cybersecurity defences to measures for balancing security and privacy interests.Read moreRead less
Machine learning in adversarial environments. Machine learning underpins the technologies driving the economies of both Silicon Valley and Wall Street, from web search and ad placement, to stock predictions and efforts in fighting cybercrime. This project aims to answer the question: How can machines learn from data when contributors act maliciously for personal gain?