Project acronym 3D Reloaded
Project 3D Reloaded: Novel Algorithms for 3D Shape Inference and Analysis
Researcher (PI) Daniel Cremers
Host Institution (HI) TECHNISCHE UNIVERSITAET MUENCHEN
Country Germany
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.
In this project, we will focus on three lines of research:
A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.
B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).
C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.
Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.
Summary
Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.
In this project, we will focus on three lines of research:
A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.
B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).
C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.
Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.
Max ERC Funding
2 000 000 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym ACCORD
Project Algorithms for Complex Collective Decisions on Structured Domains
Researcher (PI) Edith Elkind
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Algorithms for Complex Collective Decisions on Structured Domains.
The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.
Summary
Algorithms for Complex Collective Decisions on Structured Domains.
The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.
Max ERC Funding
1 395 933 €
Duration
Start date: 2015-07-01, End date: 2020-12-31
Project acronym AI4REASON
Project Artificial Intelligence for Large-Scale Computer-Assisted Reasoning
Researcher (PI) Josef Urban
Host Institution (HI) CESKE VYSOKE UCENI TECHNICKE V PRAZE
Country Czechia
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary The goal of the AI4REASON project is a breakthrough in what is considered a very hard problem in AI and automation of reasoning, namely the problem of automatically proving theorems in large and complex theories. Such complex formal theories arise in projects aimed at verification of today's advanced mathematics such as the Formal Proof of the Kepler Conjecture (Flyspeck), verification of software and hardware designs such as the seL4 operating system kernel, and verification of other advanced systems and technologies on which today's information society critically depends.
It seems extremely complex and unlikely to design an explicitly programmed solution to the problem. However, we have recently demonstrated that the performance of existing approaches can be multiplied by data-driven AI methods that learn reasoning guidance from large proof corpora. The breakthrough will be achieved by developing such novel AI methods. First, we will devise suitable Automated Reasoning and Machine Learning methods that learn reasoning knowledge and steer the reasoning processes at various levels of granularity. Second, we will combine them into autonomous self-improving AI systems that interleave deduction and learning in positive feedback loops. Third, we will develop approaches that aggregate reasoning knowledge across many formal, semi-formal and informal corpora and deploy the methods as strong automation services for the formal proof community.
The expected outcome is our ability to prove automatically at least 50% more theorems in high-assurance projects such as Flyspeck and seL4, bringing a major breakthrough in formal reasoning and verification. As an AI effort, the project offers a unique path to large-scale semantic AI. The formal corpora concentrate centuries of deep human thinking in a computer-understandable form on which deductive and inductive AI can be combined and co-evolved, providing new insights into how humans do mathematics and science.
Summary
The goal of the AI4REASON project is a breakthrough in what is considered a very hard problem in AI and automation of reasoning, namely the problem of automatically proving theorems in large and complex theories. Such complex formal theories arise in projects aimed at verification of today's advanced mathematics such as the Formal Proof of the Kepler Conjecture (Flyspeck), verification of software and hardware designs such as the seL4 operating system kernel, and verification of other advanced systems and technologies on which today's information society critically depends.
It seems extremely complex and unlikely to design an explicitly programmed solution to the problem. However, we have recently demonstrated that the performance of existing approaches can be multiplied by data-driven AI methods that learn reasoning guidance from large proof corpora. The breakthrough will be achieved by developing such novel AI methods. First, we will devise suitable Automated Reasoning and Machine Learning methods that learn reasoning knowledge and steer the reasoning processes at various levels of granularity. Second, we will combine them into autonomous self-improving AI systems that interleave deduction and learning in positive feedback loops. Third, we will develop approaches that aggregate reasoning knowledge across many formal, semi-formal and informal corpora and deploy the methods as strong automation services for the formal proof community.
The expected outcome is our ability to prove automatically at least 50% more theorems in high-assurance projects such as Flyspeck and seL4, bringing a major breakthrough in formal reasoning and verification. As an AI effort, the project offers a unique path to large-scale semantic AI. The formal corpora concentrate centuries of deep human thinking in a computer-understandable form on which deductive and inductive AI can be combined and co-evolved, providing new insights into how humans do mathematics and science.
Max ERC Funding
1 499 500 €
Duration
Start date: 2015-09-01, End date: 2020-10-31
Project acronym AlmaCrypt
Project Algorithmic and Mathematical Cryptology
Researcher (PI) Antoine Joux
Host Institution (HI) CISPA - HELMHOLTZ-ZENTRUM FUR INFORMATIONSSICHERHEIT GGMBH
Country Germany
Call Details Advanced Grant (AdG), PE6, ERC-2014-ADG
Summary Cryptology is a foundation of information security in the digital world. Today's internet is protected by a form of cryptography based on complexity theoretic hardness assumptions. Ideally, they should be strong to ensure security and versatile to offer a wide range of functionalities and allow efficient implementations. However, these assumptions are largely untested and internet security could be built on sand.
The main ambition of Almacrypt is to remedy this issue by challenging the assumptions through an advanced algorithmic analysis.
In particular, this proposal questions the two pillars of public-key encryption: factoring and discrete logarithms. Recently, the PI contributed to show that in some cases, the discrete logarithm problem is considerably weaker than previously assumed. A main objective is to ponder the security of other cases of the discrete logarithm problem, including elliptic curves, and of factoring. We will study the generalization of the recent techniques and search for new algorithmic options with comparable or better efficiency.
We will also study hardness assumptions based on codes and subset-sum, two candidates for post-quantum cryptography. We will consider the applicability of recent algorithmic and mathematical techniques to the resolution of the corresponding putative hard problems, refine the analysis of the algorithms and design new algorithm tools.
Cryptology is not limited to the above assumptions: other hard problems have been proposed to aim at post-quantum security and/or to offer extra functionalities. Should the security of these other assumptions become critical, they would be added to Almacrypt's scope. They could also serve to demonstrate other applications of our algorithmic progress.
In addition to its scientific goal, Almacrypt also aims at seeding a strengthened research community dedicated to algorithmic and mathematical cryptology.
--
Summary
Cryptology is a foundation of information security in the digital world. Today's internet is protected by a form of cryptography based on complexity theoretic hardness assumptions. Ideally, they should be strong to ensure security and versatile to offer a wide range of functionalities and allow efficient implementations. However, these assumptions are largely untested and internet security could be built on sand.
The main ambition of Almacrypt is to remedy this issue by challenging the assumptions through an advanced algorithmic analysis.
In particular, this proposal questions the two pillars of public-key encryption: factoring and discrete logarithms. Recently, the PI contributed to show that in some cases, the discrete logarithm problem is considerably weaker than previously assumed. A main objective is to ponder the security of other cases of the discrete logarithm problem, including elliptic curves, and of factoring. We will study the generalization of the recent techniques and search for new algorithmic options with comparable or better efficiency.
We will also study hardness assumptions based on codes and subset-sum, two candidates for post-quantum cryptography. We will consider the applicability of recent algorithmic and mathematical techniques to the resolution of the corresponding putative hard problems, refine the analysis of the algorithms and design new algorithm tools.
Cryptology is not limited to the above assumptions: other hard problems have been proposed to aim at post-quantum security and/or to offer extra functionalities. Should the security of these other assumptions become critical, they would be added to Almacrypt's scope. They could also serve to demonstrate other applications of our algorithmic progress.
In addition to its scientific goal, Almacrypt also aims at seeding a strengthened research community dedicated to algorithmic and mathematical cryptology.
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Max ERC Funding
2 403 125 €
Duration
Start date: 2016-01-01, End date: 2022-06-30
Project acronym AMPLify
Project Allocation Made PracticaL
Researcher (PI) Toby Walsh
Host Institution (HI) TECHNISCHE UNIVERSITAT BERLIN
Country Germany
Call Details Advanced Grant (AdG), PE6, ERC-2014-ADG
Summary Allocation Made PracticaL
The AMPLify project will lay the foundations of a new field, computational behavioural game theory that brings a computational perspective, computational implementation, and behavioural insights to game theory. These foundations will be laid by tackling a pressing problem facing society today: the efficient and fair allocation of resources and costs. Research in allocation has previously considered simple, abstract models like cake cutting. We propose to develop richer models that capture important new features like asynchronicity which occur in many markets being developed in our highly connected and online world. The mechanisms currently used to allocate resources and costs are limited to these simple, abstract models and also do not take into account how people actually behave in practice. We will therefore design new mechanisms for these richer allocation problems that exploit insights gained from behavioural game theory like loss aversion. We will also tackle the complexity of these rich models and mechanisms with computational tools. Finally, we will use computation to increase both the efficiency and fairness of allocations. As a result, we will be able to do more with fewer resources and greater fairness. Our initial case studies in resource and cost allocation demonstrate that we can improve efficiency greatly, offering one company alone savings of up to 10% (which is worth tens of millions of dollars every year). We predict even greater impact with the more sophisticated mechanisms to be developed during the course of this project.
Summary
Allocation Made PracticaL
The AMPLify project will lay the foundations of a new field, computational behavioural game theory that brings a computational perspective, computational implementation, and behavioural insights to game theory. These foundations will be laid by tackling a pressing problem facing society today: the efficient and fair allocation of resources and costs. Research in allocation has previously considered simple, abstract models like cake cutting. We propose to develop richer models that capture important new features like asynchronicity which occur in many markets being developed in our highly connected and online world. The mechanisms currently used to allocate resources and costs are limited to these simple, abstract models and also do not take into account how people actually behave in practice. We will therefore design new mechanisms for these richer allocation problems that exploit insights gained from behavioural game theory like loss aversion. We will also tackle the complexity of these rich models and mechanisms with computational tools. Finally, we will use computation to increase both the efficiency and fairness of allocations. As a result, we will be able to do more with fewer resources and greater fairness. Our initial case studies in resource and cost allocation demonstrate that we can improve efficiency greatly, offering one company alone savings of up to 10% (which is worth tens of millions of dollars every year). We predict even greater impact with the more sophisticated mechanisms to be developed during the course of this project.
Max ERC Funding
2 499 681 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym aSCEND
Project Secure Computation on Encrypted Data
Researcher (PI) Hoe Teck Wee
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Recent trends in computing have prompted users and organizations to store an increasingly large amount of sensitive data at third party locations in the cloud outside of their direct control. Storing data remotely poses an acute security threat as these data are outside our control and could potentially be accessed by untrusted parties. Indeed, the reality of these threats have been borne out by the Snowden leaks and hundreds of data breaches each year. In order to protect our data, we will need to encrypt it.
Functional encryption is a novel paradigm for public-key encryption that enables both fine-grained access control and selective computation on encrypted data, as is necessary to protect big, complex data in the cloud. Functional encryption also enables searches on encrypted travel records and surveillance video as well as medical studies on encrypted medical records in a privacy-preserving manner; we can give out restricted secret keys that reveal only the outcome of specific searches and tests. These mechanisms allow us to maintain public safety without compromising on civil liberties, and to facilitate medical break-throughs without compromising on individual privacy.
The goals of the aSCEND project are (i) to design pairing and lattice-based functional encryption that are more efficient and ultimately viable in practice; and (ii) to obtain a richer understanding of expressive functional encryption schemes and to push the boundaries from encrypting data to encrypting software. My long-term vision is the ubiquitous use of functional encryption to secure our data and our computation, just as public-key encryption is widely used today to secure our communication. Realizing this vision requires new advances in the foundations of functional encryption, which is the target of this project.
Summary
Recent trends in computing have prompted users and organizations to store an increasingly large amount of sensitive data at third party locations in the cloud outside of their direct control. Storing data remotely poses an acute security threat as these data are outside our control and could potentially be accessed by untrusted parties. Indeed, the reality of these threats have been borne out by the Snowden leaks and hundreds of data breaches each year. In order to protect our data, we will need to encrypt it.
Functional encryption is a novel paradigm for public-key encryption that enables both fine-grained access control and selective computation on encrypted data, as is necessary to protect big, complex data in the cloud. Functional encryption also enables searches on encrypted travel records and surveillance video as well as medical studies on encrypted medical records in a privacy-preserving manner; we can give out restricted secret keys that reveal only the outcome of specific searches and tests. These mechanisms allow us to maintain public safety without compromising on civil liberties, and to facilitate medical break-throughs without compromising on individual privacy.
The goals of the aSCEND project are (i) to design pairing and lattice-based functional encryption that are more efficient and ultimately viable in practice; and (ii) to obtain a richer understanding of expressive functional encryption schemes and to push the boundaries from encrypting data to encrypting software. My long-term vision is the ubiquitous use of functional encryption to secure our data and our computation, just as public-key encryption is widely used today to secure our communication. Realizing this vision requires new advances in the foundations of functional encryption, which is the target of this project.
Max ERC Funding
1 253 893 €
Duration
Start date: 2015-06-01, End date: 2021-05-31
Project acronym AUTAR
Project A Unified Theory of Algorithmic Relaxations
Researcher (PI) Albert Atserias Peri
Host Institution (HI) UNIVERSITAT POLITECNICA DE CATALUNYA
Country Spain
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary For a large family of computational problems collectively known as constrained optimization and satisfaction problems (CSPs), four decades of research in algorithms and computational complexity have led to a theory that tries to classify them as algorithmically tractable vs. intractable, i.e. polynomial-time solvable vs. NP-hard. However, there remains an important gap in our knowledge in that many CSPs of interest resist classification by this theory. Some such problems of practical relevance include fundamental partition problems in graph theory, isomorphism problems in combinatorics, and strategy-design problems in mathematical game theory. To tackle this gap in our knowledge, the research of the last decade has been driven either by finding hard instances for algorithms that solve tighter and tighter relaxations of the original problem, or by formulating new hardness-hypotheses that are stronger but admittedly less robust than NP-hardness.
The ultimate goal of this project is closing the gap between the partial progress that these approaches represent and the original classification project into tractable vs. intractable problems. Our thesis is that the field has reached a point where, in many cases of interest, the analysis of the current candidate algorithms that appear to solve all instances could suffice to classify the problem one way or the other, without the need for alternative hardness-hypotheses. The novelty in our approach is a program to develop our recent discovery that, in some cases of interest, two methods from different areas match in strength: indistinguishability pebble games from mathematical logic, and hierarchies of convex relaxations from mathematical programming. Thus, we aim at making significant advances in the status of important algorithmic problems by looking for a general theory that unifies and goes beyond the current understanding of its components.
Summary
For a large family of computational problems collectively known as constrained optimization and satisfaction problems (CSPs), four decades of research in algorithms and computational complexity have led to a theory that tries to classify them as algorithmically tractable vs. intractable, i.e. polynomial-time solvable vs. NP-hard. However, there remains an important gap in our knowledge in that many CSPs of interest resist classification by this theory. Some such problems of practical relevance include fundamental partition problems in graph theory, isomorphism problems in combinatorics, and strategy-design problems in mathematical game theory. To tackle this gap in our knowledge, the research of the last decade has been driven either by finding hard instances for algorithms that solve tighter and tighter relaxations of the original problem, or by formulating new hardness-hypotheses that are stronger but admittedly less robust than NP-hardness.
The ultimate goal of this project is closing the gap between the partial progress that these approaches represent and the original classification project into tractable vs. intractable problems. Our thesis is that the field has reached a point where, in many cases of interest, the analysis of the current candidate algorithms that appear to solve all instances could suffice to classify the problem one way or the other, without the need for alternative hardness-hypotheses. The novelty in our approach is a program to develop our recent discovery that, in some cases of interest, two methods from different areas match in strength: indistinguishability pebble games from mathematical logic, and hierarchies of convex relaxations from mathematical programming. Thus, we aim at making significant advances in the status of important algorithmic problems by looking for a general theory that unifies and goes beyond the current understanding of its components.
Max ERC Funding
1 725 656 €
Duration
Start date: 2015-06-01, End date: 2020-09-30
Project acronym AVS-ISS
Project Analysis, Verification, and Synthesis for Infinite-State Systems
Researcher (PI) Joel Olivier Ouaknine
Host Institution (HI) MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Country Germany
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary The central objective of this project is to investigate key algorithmic verification questions concerning two fundamental mathematical structures used to model and analyse infinite-state systems, namely discrete linear dynamical systems and counter automata, in both ordinary and parametric form. Motivated especially by applications to software model checking (more specifically the termination of linear loops and predicate abstraction computations), as well as parametric real-time reasoning and the verification of Markov chains, we will focus on model-checking, module-checking, and synthesis problems for linear dynamical systems and one-counter automata against various fragments and extensions of Linear Temporal Logic (LTL) specifications. The key deliverables will be novel verification algorithms along with a map of the complexity landscape. A second objective is then to transfer algorithmic insights into practical verification methodologies and tools, in collaboration with colleagues in academia and industrial research laboratories.
We will build on a series of recent advances and breakthroughs in these areas (some of which from the PI’s team) to attack a range of specific algorithmic problems. We believe that this line of research will not only result in fundamental theoretical contributions and insights in their own right—potentially answering mathematical questions that have been open for years or even decades—but will also impact the practice of formal verification and lead to new and more powerful methods and tools for the use of engineers and programmers.
Summary
The central objective of this project is to investigate key algorithmic verification questions concerning two fundamental mathematical structures used to model and analyse infinite-state systems, namely discrete linear dynamical systems and counter automata, in both ordinary and parametric form. Motivated especially by applications to software model checking (more specifically the termination of linear loops and predicate abstraction computations), as well as parametric real-time reasoning and the verification of Markov chains, we will focus on model-checking, module-checking, and synthesis problems for linear dynamical systems and one-counter automata against various fragments and extensions of Linear Temporal Logic (LTL) specifications. The key deliverables will be novel verification algorithms along with a map of the complexity landscape. A second objective is then to transfer algorithmic insights into practical verification methodologies and tools, in collaboration with colleagues in academia and industrial research laboratories.
We will build on a series of recent advances and breakthroughs in these areas (some of which from the PI’s team) to attack a range of specific algorithmic problems. We believe that this line of research will not only result in fundamental theoretical contributions and insights in their own right—potentially answering mathematical questions that have been open for years or even decades—but will also impact the practice of formal verification and lead to new and more powerful methods and tools for the use of engineers and programmers.
Max ERC Funding
1 834 975 €
Duration
Start date: 2015-08-01, End date: 2021-01-31
Project acronym BASTION
Project Leveraging Binary Analysis to Secure the Internet of Things
Researcher (PI) Thorsten Holz
Host Institution (HI) RUHR-UNIVERSITAET BOCHUM
Country Germany
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary We are in the midst of the shift towards the Internet of Things (IoT), where more and more (legacy) devices are connected to the Internet and communicate with each other. This paradigm shift brings new security challenges and unfortunately many current security solutions are not applicable anymore, e.g., because of a lack of clear network boundaries or resource-constrained devices. However, security plays a central role: In addition to its classical function in protecting against manipulation and fraud, it also enables novel applications and innovative business models.
We propose a research program that leverages binary analysis techniques to improve the security within the IoT. We concentrate on the software level since this enables us to both analyze a given device for potential security vulnerabilities and add security features to harden the device against future attacks. More specifically, we concentrate on the firmware (i.e., the combination of persistent memory together with program code and data that powers such devices) and develop novel mechanism for binary analysis of such software. We design an intermediate language to abstract away from the concrete assembly level and this enables an analysis of many different platforms within a unified analysis framework. We transfer and extend program analysis techniques such as control-/data-flow analysis or symbolic execution and apply them to our IL. Given this novel toolset, we can analyze security properties of a given firmware image (e.g., uncovering undocumented functionality and detecting memory corruption or logical vulnerabilities,). We also explore how to harden a firmware by retrofitting security mechanisms (e.g., adding control-flow integrity or automatically eliminating unnecessary functionality). This research will deepen our fundamental understanding of binary analysis methods and apply it to a novel area as it lays the foundations of performing this analysis on the level of intermediate languages.
Summary
We are in the midst of the shift towards the Internet of Things (IoT), where more and more (legacy) devices are connected to the Internet and communicate with each other. This paradigm shift brings new security challenges and unfortunately many current security solutions are not applicable anymore, e.g., because of a lack of clear network boundaries or resource-constrained devices. However, security plays a central role: In addition to its classical function in protecting against manipulation and fraud, it also enables novel applications and innovative business models.
We propose a research program that leverages binary analysis techniques to improve the security within the IoT. We concentrate on the software level since this enables us to both analyze a given device for potential security vulnerabilities and add security features to harden the device against future attacks. More specifically, we concentrate on the firmware (i.e., the combination of persistent memory together with program code and data that powers such devices) and develop novel mechanism for binary analysis of such software. We design an intermediate language to abstract away from the concrete assembly level and this enables an analysis of many different platforms within a unified analysis framework. We transfer and extend program analysis techniques such as control-/data-flow analysis or symbolic execution and apply them to our IL. Given this novel toolset, we can analyze security properties of a given firmware image (e.g., uncovering undocumented functionality and detecting memory corruption or logical vulnerabilities,). We also explore how to harden a firmware by retrofitting security mechanisms (e.g., adding control-flow integrity or automatically eliminating unnecessary functionality). This research will deepen our fundamental understanding of binary analysis methods and apply it to a novel area as it lays the foundations of performing this analysis on the level of intermediate languages.
Max ERC Funding
1 472 269 €
Duration
Start date: 2015-03-01, End date: 2020-02-29
Project acronym Big Splash
Project Big Splash: Efficient Simulation of Natural Phenomena at Extremely Large Scales
Researcher (PI) Christopher John Wojtan
Host Institution (HI) INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
Country Austria
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source.
I propose two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. My first avenue of research combines numerics and shape: I will investigate a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. I will also develop methods for merging small-scale analytical solutions with large-scale numerical algorithms. (These ideas show particular promise for phenomena like splashing liquids and fracturing solids, whose small-scale behaviors are poorly captured by standard finite element methods.) My second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, we will drastically speed up computation with novel dimension reduction and data compression approaches. We can also minimize unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work will undoubtedly synergize to enable the simulation and understanding of complicated natural and biological processes that are presently unfeasible to compute.
Summary
Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source.
I propose two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. My first avenue of research combines numerics and shape: I will investigate a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. I will also develop methods for merging small-scale analytical solutions with large-scale numerical algorithms. (These ideas show particular promise for phenomena like splashing liquids and fracturing solids, whose small-scale behaviors are poorly captured by standard finite element methods.) My second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, we will drastically speed up computation with novel dimension reduction and data compression approaches. We can also minimize unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work will undoubtedly synergize to enable the simulation and understanding of complicated natural and biological processes that are presently unfeasible to compute.
Max ERC Funding
1 500 000 €
Duration
Start date: 2015-03-01, End date: 2020-08-31
