KeyForge: Mitigating Email Breaches with Forward-Forgeable Signatures

Email breaches are commonplace, and they expose a wealth of personal, business, and political data that may have devastating consequences. The current email system allows any attacker who gains access to your email to prove the authenticity of the stolen messages to third parties -- a property arising from a necessary anti-spam / anti-spoofing protocol called DKIM. This exacerbates the problem of email breaches by greatly increasing the potential for attackers to damage the users' reputation, blackmail them, or sell the stolen information to third parties. In this paper, we introduce "non-attributable email", which guarantees that a wide class of adversaries are unable to convince any third party of the authenticity of stolen emails. We formally define non-attributability, and present two practical system proposals -- KeyForge and TimeForge -- that provably achieve non-attributability while maintaining the important protection against spam and spoofing that is currently provided by DKIM. Moreover, we implement KeyForge and demonstrate that that scheme is practical, achieving competitive verification and signing speed while also requiring 42% less bandwidth per email than RSA2048

Applied Formal Methods in Wireless Sensor Networks

This work covers the application of formal methods to the world of wireless sensor networks. Mainly two different perspectives are analyzed through mathematical models which can be distinct for example into qualitative statements like "Is the system error free?" From the perspective of quantitative propositions we investigate protocol optimal parameter settings for an energy efficient operation

Rationality and Efficient Verifiable Computation

In this thesis, we study protocols for delegating computation in a model where one of the parties is rational. In our model, a delegator outsources the computation of a function f on input x to a worker, who receives a (possibly monetary) reward. Our goal is to design very efficient delegation schemes where a worker is economically incentivized to provide the correct result f(x). In this work we strive for not relying on cryptographic assumptions, in particular our results do not require the existence of one-way functions. We provide several results within the framework of rational proofs introduced by Azar and Micali (STOC 2012).We make several contributions to efficient rational proofs for general feasible computations. First, we design schemes with a sublinear verifier with low round and communication complexity for space-bounded computations. Second, we provide evidence, as lower bounds, against the existence of rational proofs: with logarithmic communication and polylogarithmic verification for P and with polylogarithmic communication for NP. We then move to study the case where a delegator outsources multiple inputs. First, we formalize an extended notion of rational proofs for this scenario (sequential composability) and we show that existing schemes do not satisfy it. We show how these protocols incentivize workers to provide many ``fast\u27\u27 incorrect answers which allow them to solve more problems and collect more rewards. We then design a d-rounds rational proof for sufficiently ``regular\u27\u27 arithmetic circuit of depth d = O(log(n)) with sublinear verification. We show, that under certain cost assumptions, our scheme is sequentially composable, i.e. it can be used to delegate multiple inputs. We finally show that our scheme for space-bounded computations is also sequentially composable under certain cost assumptions. In the last part of this thesis we initiate the study of Fine Grained Secure Computation: i.e. the construction of secure computation primitives against ``moderately complex adversaries. Such fine-grained protocols can be used to obtain sequentially composable rational proofs. We present definitions and constructions for compact Fully Homomorphic Encryption and Verifiable Computation secure against (non-uniform) NC1 adversaries. Our results hold under a widely believed separation assumption implied by L ≠NC1 . We also present two application scenarios for our model: (i) hardware chips that prove their own correctness, and (ii) protocols against rational adversaries potentially relevant to the Verifier\u27s Dilemma in smart-contracts transactions such as Ethereum

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., J. Phys. Condens. Matter 29, 273002 (2017)) and i-PI (Kapil et al., Comput. Phys. Commun. 236, 214-223 (2019)) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations

Model-based planning through constraint and causal order decomposition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 95-98).One of the major challenges in autonomous planning and sequencing is the theoretical complexity of planning problems. Even a simple STRIPS planning problem is PSPACEcomplete, and depending on the expressivity of the planning problem, the complexity of the problem can be EXPTIME-complete or worse. This thesis improves on current approaches to sequencing the engineering operations of a spacecraft or ground-based asset through the explicit use of verifiable models and a decomposition approach to planning. Based on specifications of system behavior, the planner generates control sequences of engineering operations that achieve mission objectives specified by an operator. This work is novel in three ways. First, an innovative "divide-and-conquer" approach is used to assure efficiency and scalability of the planner. The key to the approach is in its combined use of constraint decomposition and causal order decomposition. This technique provides the means to decompose the problem into a set of subproblems and to identify the ordering by which each subproblem should be solved, thus reducing, and possibly eliminating, search. Second, the decomposed planning framework is able to solve complex planning problems with state constraints and temporally extended goals. Such complex system behavior is specified as concurrent, constraint automata (CCA) that provide the expressiveness necessary to model the behavior of the system components and their interactions. The mission objective is described as a desired evolution of goal states called a qualitative state plan (QSP), explicitly capturing the intent of the operators. Finally, the planner generates a partially-ordered plan called a qualitative control plan (QCP) that provides additional execution robustness through temporal flexibility. We demonstrate the decomposed approach to Model-based planning on a scenario based on the ongoing Autonomous Sciencecraft Experiment, onboard EO-1 spacecraft. The EO-1 problem has a large state space with well over 660 quintillion states, 6.6 x 10²⁰.(cont.) Despite the size and the complexity of the problem, the time performance is linear in the length of the plan and the memory usage is linear in the number of components.by Seung H. Chung.Ph.D