Side channel attacks use, alongside information such as plaintexts or ciphertexts, leakage about the (secret) key-dependent intermediate state(s), and deliver a `key ranking' as a result. Kocher's attacks [15] [16] showed that for many practical implementations, observing a few encryptions made complete key recovery possible in practice. The academic research into combating these attacks so far has largely focused on approaches and tools to equip specialised cryptographic engineers with access to a specialist lab and tools. The research hypothesis of this CoG is that one can make meaningful statements about the leakage behaviour of arbitrary implementations on small devices by utilising a-priori derived (instruction level) leakage models. Our vision is to enable developers with limited domain-specific knowledge to perform side channel evaluations at design time without access to a fully equipped lab, by creating tools and methodologies that integrate a priori derived instruction-level leakage models into a standard compiler. This vision is articulated in three overarching research objectives: 1. Designing novel profiling strategies (WP1) including novel leakage acquisition techniques to generate leakage models for a specific target device. 2. Developing fast and comprehensive methods to support rapid evaluations (WP2). 3. Integration (WP3) of semantics, syntax and tools capable of using profiling information into a standard compiler with the aim to evaluate and improve the side channel resilience of the target code. Addressing these goals simultaneously is required to make substantial progress towards the overall vision of this project. As a final result, we will make demonstrators available (WP4): using a off-the shelf components, we supply the necessary tools and compiler enhancements including samples of cryptographic implementations to conduct analyses and demonstrate improvements regarding side channel resilience.

A forest transition, i.e. forest expansion after a long period of deforestation, has occurred in many, mostly industrialized countries. Forest transitions have recently resulted in declining rates of global net deforestation and contributed to carbon (C) sinks in terrestrial ecosystems. Studies have shown the concurrence of forest transitions and industrialization processes, but the systemic links between forest transitions, their underlying socio-metabolic processes and associated greenhouse gas (GHG) emissions have been neither systematically explored nor quantified. HEFT introduces the idea of “hidden emissions of forest transitions”, i.e. the GHG emissions from socio-metabolic processes reducing pressures on forests. Hidden emissions may stem from processes such as substitution of fuelwood by modern energy sources, intensification of agriculture, and externalization of biomass production to remote regions. Building on the concept of socio-ecological metabolism, HEFT develops a consistent methodological framework to quantify the full GHG emissions and sinks from socio-metabolic and ecological processes in the course of forest transitions, within which their hidden emissions are identified. Forest transitions in multiple contexts are analyzed at local, national and supranational scales: in Europe since c. 1850, North America since c. 1880, and South East Asia since 1980. A coarse global-scale assessment complements the regional case studies. We will integrate sources and analytical methods from environmental and social sciences as well as the humanities to analyze context-specific trajectories and general features of socio-ecological GHG budgets and their respective socio-political contexts since the onset of forest transitions. The sound understanding of hidden emissions will be used to identify the least GHG-intensive trajectories and to draw lessons for future climate-friendly forest transitions.

Sustainability transformations imply fundamental changes in the societal use of biophysical resources. Current socioeconomic metabolism research traces flows of energy, materials or substances to capture resource use: input of raw materials or energy, their fate in production and consumption, and the discharge of wastes and emissions. This approach has yielded important insights into eco-efficiency and long-term drivers of resource use. But due to its focus on flows, socio-metabolic research has not yet incorporated material stocks or their services, thereby not fully realizing its analytic potential. MAT_STOCKS addresses this gap by developing a consistent typology, indicators and databases of material stocks and their services, building upon economy-wide material flow analysis. It will create a comprehensive, global, national-level, validated material stocks and services database as well as maps of material stocks from remote-sensing data. This will allow analyzing the stock/flow/service nexus and underpin highly innovative indicators of eco-efficiency overcoming limitations of current approaches which mainly relate resource use or emissions to population and GDP. New insights on stock/flow/service relations, the relevance of spatial patterns and options for decoupling will be used to create a dynamic model to assess option spaces for transformations towards sustainable metabolism. MAT_STOCKS will identify barriers and leverage points for future sustainability transformations and the SDGs, and elucidate their socio-ecological and political implications. Our preliminary analyses suggest that unravelling the stock/flow/service nexus provides a crucial missing link in socio-metabolic research because it explains why, how and where patterns of material and energy use change or remain locked-in. Thereby, important analytical insights will be introduced into the largely normative and local discourses on the transformation towards a sustainable society.