Michael T. Goodrich

Alvin Chiu, David Eppstein, Michael T. Goodrich et al.

We show how to visualize a graph, $G=(V,E)$, as a layered drawing, layer-respecting arc diagram, or layer-respecting linear cylindric drawing with a minimum number of edge crossings, where layer-respecting means that layers appear in order on a single line and vertices are grouped by their layers. Even though this problem is NP-hard for general arc diagrams, we show how to create such diagrams with fixed-parameter tractable linear-time algorithms, where the parameter that allows this is the width of a layered graph. Such a layered graph can be obtained from a breadth-first search (BFS), in which case the width is upper bounded by a graph width parameter called the BFS width.

Alvin Chiu, Mithun Ghosh, Reyan Ahmed et al.

Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a sparsifier. The proposed method consistently outperforms several standard approximation algorithms on different types of graphs and often finds the optimal solution.

Michael Goodrich, Jacob Crandall, Aaron Steinfeld et al.

The proposed symposium focuses understanding, modeling, and improving the efficacy of (a) communicating proficiency from human to robot and (b) communicating intent from a human to a robot. For example, how should a robot convey predicted ability on a new task? How should it report performance on a task that was just completed? How should a robot adapt its proficiency criteria based on human intentions and values? Communities in AI, robotics, HRI, and cognitive science have addressed related questions, but there are no agreed upon standards for evaluating proficiency and intent-based interactions. This is a pressing challenge for human-robot interaction for a variety of reasons. Prior work has shown that a robot that can assess its performance can alter human perception of the robot and decisions on control allocation. There is also significant evidence in robotics that accurately setting human expectations is critical, especially when proficiency is below human expectations. Moreover, proficiency assessment depends on context and intent, and a human teammate might increase or decrease performance standards, adapt tolerance for risk and uncertainty, demand predictive assessments that affect attention allocation, or otherwise reassess or adapt intent.

Michael T. Goodrich, Vinesh Sridhar

Embedded systems and Internet of Things (IoT) applications motivate in-place parallel algorithms, which avoid allocating additional shared memory past the input. Work by Gu, Obeya, and Shun [APOCS '21] defines a family of PIP (parallel in-place) models and parallel algorithms that eschew auxiliary memory at high processor counts while remaining in-situ when run sequentially. However, their models assume asynchronous processing and have no in-place guarantees for intermediate processor counts. We address this gap in the literature by proposing a Synchronous PIP family of models for in-place parallel and distributed computation. We demonstrate the effectiveness of our new model by giving efficient and synchronous parallel algorithms in this model that require no auxiliary shared memory and only constant private memory per processor. Importantly, we show how to leverage a new parallel-augmented sweep technique to ensure that Synchronous PIP algorithms remain efficient and strictly in-place at all processor counts.

Michael T. Goodrich

Algorithms for oblivious random access machine (ORAM) simulation allow a client, Alice, to obfuscate a pattern of data accesses with a server, Bob, who is maintaining Alice's outsourced data while trying to learn information about her data. We present a novel ORAM scheme that improves the asymptotic I/O overhead of previous schemes for a wide range of size parameters for client-side private memory and message blocks, from logarithmic to polynomial. Our method achieves statistical security for hiding Alice's access pattern and, with high probability, achieves an I/O overhead that ranges from $O(1)$ to $O(\log^2 n/(\log\log n)^2)$, depending on these size parameters, where $n$ is the size of Alice's outsourced memory. Our scheme, which we call BIOS ORAM, combines multiple uses of B-trees with a reduction of ORAM simulation to isogrammic access sequences.

Fattaneh Bayatbabolghani, Marina Blanton, Mehrdad Aliasgari et al.

We present three private fingerprint alignment and matching protocols, based on what are considered to be the most precise and efficient fingerprint recognition algorithms, which use minutia points. Our protocols allow two or more honest-but-curious parties to compare their respective privately-held fingerprints in a secure way such that they each learn nothing more than an accurate score of how well the fingerprints match. To the best of our knowledge, this is the first time fingerprint alignment based on minutiae is considered in a secure computation framework. We build secure fingerprint alignment and matching protocols in both the two-party setting using garbled circuit evaluation and in the multi-party setting using secret sharing techniques. In addition to providing precise and efficient secure fingerprint alignment and matching, our contributions include the design of a number of secure sub-protocols for complex operations such as sine, cosine, arctangent, square root, and selection, which are likely to be of independent interest.

David Eppstein, Michael T. Goodrich, Jenny Lam et al.

We introduce models and algorithmic foundations for graph watermarking. Our frameworks include security definitions and proofs, as well as characterizations when graph watermarking is algorithmically feasible, in spite of the fact that the general problem is NP-complete by simple reductions from the subgraph isomorphism or graph edit distance problems. In the digital watermarking of many types of files, an implicit step in the recovery of a watermark is the mapping of individual pieces of data, such as image pixels or movie frames, from one object to another. In graphs, this step corresponds to approximately matching vertices of one graph to another based on graph invariants such as vertex degree. Our approach is based on characterizing the feasibility of graph watermarking in terms of keygen, marking, and identification functions defined over graph families with known distributions. We demonstrate the strength of this approach with exemplary watermarking schemes for two random graph models, the classic Erdős-Rényi model and a random power-law graph model, both of which are used to model real-world networks.

Olga Ohrimenko, Michael T. Goodrich, Roberto Tamassia et al.

We present a simple, efficient, and secure data-oblivious randomized shuffle algorithm. This is the first secure data-oblivious shuffle that is not based on sorting. Our method can be used to improve previous oblivious storage solutions for network-based outsourcing of data.

Michael T. Goodrich, Michael Mitzenmacher

We study the question of how to shuffle $n$ cards when faced with an opponent who knows the initial position of all the cards {\em and} can track every card when permuted, {\em except} when one takes $K< n$ cards at a time and shuffles them in a private buffer "behind your back," which we call {\em buffer shuffling}. The problem arises naturally in the context of parallel mixnet servers as well as other security applications. Our analysis is based on related analyses of load-balancing processes. We include extensions to variations that involve corrupted servers and adversarially injected messages, which correspond to an opponent who can peek at some shuffles in the buffer and who can mark some number of the cards. In addition, our analysis makes novel use of a sum-of-squares metric for anonymity, which leads to improved performance bounds for parallel mixnets and can also be used to bound well-known existing anonymity measures.

Michael T. Goodrich, Duy Nguyen, Olga Ohrimenko et al.

Searching accounts for one of the most frequently performed computations over the Internet as well as one of the most important applications of outsourced computing, producing results that critically affect users' decision-making behaviors. As such, verifying the integrity of Internet-based searches over vast amounts of web contents is essential. We provide the first solution to this general security problem. We introduce the concept of an authenticated web crawler and present the design and prototype implementation of this new concept. An authenticated web crawler is a trusted program that computes a special "signature" $s$ of a collection of web contents it visits. Subject to this signature, web searches can be verified to be correct with respect to the integrity of their produced results. This signature also allows the verification of complicated queries on web pages, such as conjunctive keyword searches. In our solution, along with the web pages that satisfy any given search query, the search engine also returns a cryptographic proof. This proof, together with the signature $s$, enables any user to efficiently verify that no legitimate web pages are omitted from the result computed by the search engine, and that no pages that are non-conforming with the query are included in the result. An important property of our solution is that the proof size and the verification time both depend solely on the sizes of the query description and the query result, but not on the number or sizes of the web pages over which the search is performed. Our authentication protocols are based on standard Merkle trees and the more involved bilinear-map accumulators. As we experimentally demonstrate, the prototype implementation of our system gives a low communication overhead between the search engine and the user, and allows for fast verification of the returned results on the user side.