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On all-or-nothing transforms and password-authenticated key exchange protocols

On all-or-nothing transforms and password-authenticated key ...

引用

作者： Boyko, Victor (Victor V.) Massachusetts Institute of Technology

学位级别：博士

This thesis provides a formal analysis of two kinds of cryptographic objects that used to be treated with much less rigor: All-or-Nothing Transforms (AONTs) and Password-Authenticated Key Exchange protocols. For both, novel formal definitions of security are given, and then practical and efficient constructions are proven secure. The constructions for password-authenticated key exchange are novel, and the AONT construction is an application of an existing scheme to a new area. AONTs have been proposed by Rivest as a mode of operation for block ciphers. An AONT is an unkeyed, invertible, randomized transformation, with the property that it is hard to invert unless all of the output is known. Applications of AONTs include improving the security and efficiency of encryption. We give several strong formal definitions of security for AONTs. We then prove that Optimal Asymmetric Encryption Padding (OAEP) satisfies these definitions (in the random oracle model). This is the first construction of an AONT that has been proven secure in the strong sense. We also show that no AONT can achieve substantially better security than OAEP. The second part of this thesis is about password-authenticated key exchange pro- tocols. We present a new protocol called PAK which is the first such Diffie-Hellman- based protocol to provide a formal proof of security (in the random oracle model) against active adversaries. In addition to the PAK protocol that provides mutual explicit authentication, we also show a more efficient protocol called PPK that is provably secure in the implicit-authentication model. We then extend PAK to a pro- tocol called PAK-X, in which one side (the client) stores a plaintext version of the password, while the other side (the server) only stores a verifier for the password. We formally prove security of PAK-X, even when the server is compromised. Our formal model for password-authenticated key exchange is new, and may be of independent interest.

关键词： electrical engineering and computer science

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Towards automated construction of compiler optimizations

Towards automated construction of compiler optimizations

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作者： Mendis, Thirimadura Charith Yasendra. Massachusetts Institute of Technology

学位级别：博士

Compilers are the workhorse that bridge the gap between human-readable and machine-executable code.e ultimate goal of a compiler is to find a legal translation that provides the most optimized machine code sequence for a given hardware platform. However, the diversity of modern programs, along with the advent of new and complex hardware architectures, has strained the capabilities of current compilers, making development and maintenance of automatic program optimizations in compilers exceedingly challenging. Despite this, compiler optimizations are still hand-crafted, using technology that existed decades ago. In this thesis, we show how to move towards more automated methods of constructing compiler optimizations, using compiler auto-vectorization as an example. Modern compilers perform vectorization using hand-crafted algorithms that typically only find local solutions under linear performance models. First, we present goSLP, a framework that uses integer linear programming to find a globally pairwise-optimal statement packing strategy to achieve superior vectorization performance. Next, we discuss how we can automatically learn how to vectorize. We present Vemal, the first end-to-end learned vectorizer, which eliminates the need for hand-writing an algorithm. The key is to formulate the optimization problem as a sequential decision making process in which all steps guarantee the correctness of the resultant generated code. Not only does Vemal reduce the need for expert design and heuristics, but it also outperforms hand-crafted algorithms, reducing developer effort while increasing performance. Finally, we show how we can use data to learn non-linear performance models that are better than the complex and incorrect hand-crafted models designed by experts, to enhance the decision procedure used in Vemal. We present Vemal, the first learned cost model for predicting the throughput of x86 code. Ithemal more than halves the error-rate of complex analytical models suc

关键词： electrical engineering and computer science

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Airblue : a highly-configurable FPGA-based platform for wireless network research

Airblue : a highly-configurable FPGA-based platform for wire...

引用

作者： Ng, Man Cheuk Massachusetts Institute of Technology

学位级别：博士

Over the past few years, researchers have developed many cross-layer wireless protocols to improve the performance of wireless networks. Experimental evaluations of these protocols require both high-speed simulations and real-time on-air experimentations. Unfortunately, radios implemented in pure software are usually inadequate for either because they are typically two to three orders of magnitude slower than commodity hardware. FPGA-based platforms provide much better speeds but are quite difficult to modify because of the way high-speed designs are typically implemented by trading modularity for performance. Experimenting with cross-layer protocols requires a flexible way to convey information beyond the data itself from lower to higher layers, and a way for higher layers to configure lower layers dynamically and within some latency bounds. One also needs to be able to modify a layer's processing pipeline without triggering a cascade of changes. In this thesis, we discuss an alternative approach to implement a high-performance yet configurable radio design on an FPGA platform that satisfies these requirements. We propose that all modules in the design must possess two important design properties, namely latency-insensitivity and datadriven control, which facilitate modular refinements. We have developed Airblue, an FPGA-based radio, that has all these properties and runs at speeds comparable to commodity hardware. Our baseline design is 802.11g compliant and is able to achieve reliable communication for bit rates up to 24 Mbps. We show in the thesis that we can implement SoftRate, a cross-layer rate adaptation protocol, by modifying only 5.6% of the source code (967 lines). We also show that our modular design approach allows us to abstract the details of the FPGA platform from the main design, thus making the design portable across multiple FPGA platforms. By taking advantage of this virtualization capability, we were able to turn Airblue into a high-speed hardwa

关键词： electrical engineering and computer science

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The logic of systems.

The logic of systems.

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作者： Furtek, Frederick Curtis Massachusetts Institute of Technology

学位级别：博士

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Causal inference: a Tensor's perspective

Causal inference: a Tensor's perspective

引用

作者： Shen, Dennis Massachusetts Institute of Technology

学位级别：博士

Quantifying the causal effect of an intervention is a ubiquitous problem that spans a wide net of applications. Typically, this quantity is measured through the difference in outcomes under treatment (e. g., novel drug) and control (e. g., placebo). However, only one outcome ever be revealed - this is the fundamental challenge in causal inference. In order to overcome this obstacle, there have been two main types of studies: experimental (ES) and observational (OS). While the former conducts carefully designed experiments, the latter utilizes observed data. In this thesis, we reinterpret the classical potential outcomes framework of Rubin through the lens of tensors. Formally, each entry of the potential outcomes tensor is associated with a tuple of entities; namely, the measurement (e. g., time), unit (e. g., patient type), and intervention (e. g., drug). Subsequently, each study can be characterized by a unique sparsity pattern, which allows us to translate the age old problem of estimating counterfactuals into one of tensor estimation. As an added benefit, our tensor formulation also opens the door to discussions about the computational and statistical trade-offs of causal inference methods, a conversation (to the best of our knowledge) that has largely not yet been had. Ultimately, this novel perspective, coupled with basic principles of the popular synthetic control method for OSs, enables us to provably estimate counterfactual potential outcomes for every unit under all treatments and control with low sample and computational complexity. As a result, we can customize treatment plans for every unit in a computationally tractable and data-efficient manner. Pleasingly, we show that this result bears implications towards what-if scenario planning, drug discovery, and personalized, data-efficient randomized control trials. Methodically, we furnish a data-driven hypothesis test to check when our algorithm can reliably recover the underlying tensor. The key technical

关键词： electrical engineering and computer science

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Learning from Ambiguity

Learning from Ambiguity

引用

作者： Maron, Oded Massachusetts Institute of Technology

学位级别：博士

There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. Each example is a bag, consisting of any number of instances. A bag is labeled negative if all instances in it are negative. A bag is labeled positive if at least one instance in it is positive. Because the instances themselves are not labeled, each positive bag is an ambiguous example. We would like to learn a concept which will correctly classify unseen bags. We have developed a measure called Diverse Density and algorithms for learning from multiple-instance examples. We have applied these techniques to problems in drug design, stock prediction, and image database retrieval. These serve as examples of how to translate the ambiguity in the application domain into bags, as well as successful examples of applying Diverse Density techniques.

关键词： electrical engineering and computer science

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Parallel algorithms for scheduling data-graph computations

Parallel algorithms for scheduling data-graph computations

引用

作者： Hasenplaugh, William Cleaburn Massachusetts Institute of Technology

学位级别：博士

A data-graph computation - popularized by such programming systems as Pregel, GraphLab, Galois, Ligra, PowerGraph, and GraphChi - is an algorithm that iteratively performs local updates on the vertices of a graph. During each round of a data-graph computation, a user-supplied update function atomically modifies the data associated with a vertex as a function of the vertex's prior data and that of adjacent vertices. A dynamic data-graph computation updates only an active subset of the vertices during a round, and those updates determine the set of active vertices for the next round. In this thesis, I explore two ways of scheduling deterministic parallel data-graph computations that provide performance guarantees culminating in theoretical contributions to graph theory and practical, high-performance systems. In particular, I describe a system called Prism which processes dynamic and static data-graph computations on arbitrary graphs using a technique called chromatic scheduling. Using a vertex-coloring to identify independent sets of vertices, which may be safely processed in parallel, Prism serializes through the colors and processes the independent sets in parallel, thus executing data-graph computations deterministically and without the use of costly atomic instructions (e.g., Compare-And-Swap). Prism supports dynamic data-graph computations deterministically and work-efficiently through the introduction of multibag and multivector data structures. Prism requires a vertex-coloring, and since graphs are generally not supplied with one, it is necessary to find one as a preprocessing step. Furthermore, the runtime of Prism is linear in the number of colors and thus motivates a study in this thesis of fast parallel coloring algorithms that provide vertex-colorings with few colors in practice. At the core of the analysis of these coloring algorithms lies a new result about the maximum depth of a random priority dag, the dag that results from randomly ordering vertices

关键词： electrical engineering and computer science

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In-situ prediction on sensor networks using distributed multiple linear regression models

In-situ prediction on sensor networks using distributed mult...

引用

作者： Basha, Elizabeth (Elizabeth Ann) Massachusetts Institute of Technology

学位级别：博士

Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation where complex models are replaced by in-situ sensing and communication. These prediction models utilize low-computation, low-communication, and distributed algorithms suited to autonomous operation and multiple applications. We achieve this through development of new algorithms that enable distributed computation of the pseudo inverse of a matrix on a sensor network, thereby enabling a wide range of prediction methods. We apply these models to three different application areas: (1) river flooding for early warning, (2) solar recharging current for power management, and (3) job congestion prediction on multi-function device networks for achieving quality of service. Additionally, we use these applications to explore other aspects of sensor networks: river flooding to design a predictive environmental monitoring sensor network, solar current to develop a dynamic version of the model for better fault tolerance, and job congestion to explore modeling multi-function device networks. For each, we comprehensively tested the full solutions. We implemented the river flood prediction and solar current prediction solutions on two different sensor network platforms with full field deployments; we had a final test of over 5 weeks operation for both. Overall, we achieve the following contributions: (1) distributed algorithms for computing a matrix pseudoinverse and multiple linear regression model on a sensor network, (2) three applications of these algorithms with associated field experiments demonstrating their versatility, (3) a sensor network architecture and implementation for river flood prediction as well as other applications requiring real-time data and a low node count to geographic area ratio, and (4) a MFD simulator predicting and resolving congestion.

关键词： electrical engineering and computer science

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Inertio-elastic focusing of bioparticles in a microchannel at ultra-high throughput

Inertio-elastic focusing of bioparticles in a microchannel a...

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作者： Lim, Eugene J. (Eugene Jungsud) Massachusetts Institute of Technology

学位级别：博士

Many biological and industrial fluids are filled with micro-scale particles that can serve as "state markers" for real-world issues, such as human health and public infrastructure. In order to extract this valuable information from such fluids, the controlled manipulation of particles is often necessary. Microfluidic technologies based on viscosity-dominant flows have achieved this essential step in small-volume (

关键词： electrical engineering and computer science

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Towards a cognitive network management and control system

Towards a cognitive network management and control system

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作者： Rezaee, Arman. Massachusetts Institute of Technology

学位级别：博士

Future networks have to accommodate an increase of 3-4 orders of magnitude in data rates with heterogeneous session sizes and strict time deadline requirements. The dynamic nature of scheduling of large transactions and the need for rapid actions by the network management and control system, require timely and judicious collection of network state information. Within this context we will focus on the problem of shortest path routing, and identify pragmatic schemes that allow a central controller to collect relevant delay statistics from various links and nodes within the network. We present Significant Sampling as an adaptive monitoring technique to collect and disseminate network state information when it can be of significant value to the optimal operation of the network, and in particular when it can help in identifying the shortest routes. We start by developing an analytical framework that can identify the optimal time for the collection of such information in a small but realistic setting, when the underlying delay model is a continuous-time diffusion process (e.g. Wiener process or Ornstein-Uhlenbeck process) and its parameters are known by the controller. We show that this technique balances the need for updated state information against the collection and dissemination costs and provides an algorithm that yields near optimum performance. We then extend the results by introducing a reinforcement learning framework that learns the aforementioned optimal policy from its own interactions with the network, and without any prior assumptions regarding the underlying delay model. In addition to achieving a performance comparable to the analytically derived policies, the deep reinforcement learning solution is more flexible and general and can accommodate a diverse set of network environments. This is particularly important because it can provide good solutions for complex network environments where analytically tractable solutions are not feasible. We conclude our

关键词： electrical engineering and computer science

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