Bo Zhu

The Simeck Family of Lightweight Block Ciphers

Two lightweight block cipher families, Simon and Speck, have been proposed by researchers from the NSA recently. In this paper, we introduce Simeck, a new family of lightweight block ciphers that combines the good design components from both Simon and Speck, in order to devise even more compact and efficient block ciphers. For Simeck32/64, we can achieve 505 GEs (before the Place and Route phase) and 549 GEs (after the Place and Route phase), with the power consumption of 0.417 \(\mu W\) in CMOS 130 nm ASIC, and 454 GEs (before the Place and Route phase) and 488 GEs (after the Place and Route phase), with the power consumption of 1.292 \(\mu W\) in CMOS 65 nm ASIC. Furthermore, all of the instances of Simeck are smaller than the ones of hardware-optimized cipher Simon in terms of area and power consumption in both CMOS 130 nm and CMOS 65 nm techniques. In addition, we also give the security evaluation of Simeck with respect to many traditional cryptanalysis methods, including differential attacks, linear attacks, impossible differential attacks, meet-in-the-middle attacks, and slide attacks. Overall, all of the instances of Simeck can satisfy the area, power, and throughput requirements in passive RFID tags.

A new grid structure for domain extension

We present an efficient grid structure that extends a uniform grid to create a significantly larger far-field grid by dynamically extending the cells surrounding a fine uniform grid while still maintaining fine resolution about the regions of interest. The far-field grid preserves almost every computational advantage of uniform grids including cache coherency, regular subdivisions for parallelization, simple data layout, the existence of efficient numerical discretizations and algorithms for solving partial differential equations, etc. This allows fluid simulations to cover large domains that are often infeasible to enclose with sufficient resolution using a uniform grid, while still effectively capturing fine scale details in regions of interest using dynamic adaptivity.

Sketch-based Dynamic Illustration of Fluid Systems

This paper presents a lightweight sketching system that enables interactive illustration of complex fluid systems. Users can sketch on a 2.5-dimensional (2.5D) canvas to design the shapes and connections of a fluid circuit. These input sketches are automatically analyzed and abstracted into a hydraulic graph, and a new hybrid fluid model is used in the background to enhance the illustrations. The system provides rich simple operations for users to edit the fluid system incrementally, and the new internal flow patterns can be simulated in real time. Our system is used to illustrate various fluid systems in medicine, biology, and engineering. We asked professional medical doctors to try our system and obtained positive feedback from them.

Image reconstruction by domain-transform manifold learning

Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy. During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain, the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction—automated transform by manifold approximation (AUTOMAP)—which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.

Codimensional surface tension flow on simplicial complexes

Many visually interesting natural phenomena are characterized by thin liquid sheets, long filaments, and droplets. We present a new Lagrangian-based numerical method to simulate these codimensional surface tension driven phenomena using non-manifold simplicial complexes. Tetrahedra, triangles, segments, and points are used to model the fluid volume, thin films, filaments, and droplets, respectively. We present a new method for enforcing fluid incompressibility on simplicial complexes along with a physically-guided meshing algorithm to provide temporally consistent information for interparticle forces. Our method naturally allows for transitions between codimensions, either from tetrahedra to triangles to segments to points or vice versa, regardless of the simulation resolution. We demonstrate the efficacy of this method by simulating various natural phenomena that are characterized by thin fluid sheets, filaments, and surface tension effects.

Multidimensional meet-in-the-middle attack and its applications to KATAN32/48/64

This paper investigates a new framework to analyze symmetric ciphers by guessing intermediate states and dividing algorithms into consecutive sub-ciphers. It is suitable for lightweight ciphers with simple key schedules and block sizes smaller than key lengths. New attacks on the block cipher family KATAN are proposed by adopting this framework. Our new attacks can recover the master keys of 175-round KATAN32, 130-round KATAN48 and 112-round KATAN64 faster than exhaustive search, and thus reach many more rounds than previous attacks. We also provide new attacks on 115-round KATAN32 and 100-round KATAN48 in order to demonstrate this new kind of attacks can be more time-efficient and memory-efficient than existing attacks.

Computational multicopter design

We present an interactive system for computational design, optimization, and fabrication of multicopters. Our computational approach allows non-experts to design, explore, and evaluate a wide range of different multicopters. We provide users with an intuitive interface for assembling a multicopter from a collection of components (e.g., propellers, motors, and carbon fiber rods). Our algorithm interactively optimizes shape and controller parameters of the current design to ensure its proper operation. In addition, we allow incorporating a variety of other metrics (such as payload, battery usage, size, and cost) into the design process and exploring tradeoffs between them. We show the efficacy of our method and system by designing, optimizing, fabricating, and operating multicopters with complex geometries and propeller configurations. We also demonstrate the ability of our optimization algorithm to improve the multicopter performance under different metrics.

A new incompressibility discretization for a hybrid particle MAC grid representation with surface tension

We take a particle based approach to incompressible free surface flow motivated by the fact that an explicit representation of the interface geometry and internal deformations gives precise feedback to an implicit solver for surface tension. Methods that enforce incompressibility directly on the particles are typically numerically inefficient compared to those that utilize a background grid. However, background grid discretizations suffer from inaccuracy near the free surface where they do not properly capture the interface geometry. Therefore, our incompressibility discretization utilizes a particle based projection near the interface and a background MAC grid based projection for efficiency in the vast interior of the liquid domain - as well as a novel method for coupling these two disparate projections together. We show that the overall coupled elliptic solver is second order accurate, and remains second order accurate when used in conjunction with an appropriate temporal discretization for parabolic problems. A similar second order accurate discretization is derived when the MAC grid unknowns are located on faces (as opposed to cell centers) so that Navier-Stokes viscosity can be solved for implicitly as well. Finally, we present a fully implicit approach to surface tension that is robust enough to achieve a steady state solution in a single time step. Beyond stable implicit surface tension for our novel hybrid discretization, we demonstrate preliminary results for both standard front tracking and the particle level set method.

Two-Scale Topology Optimization with Microstructures

In this article, we present a novel two-scale framework to optimize the structure and the material distribution of an object given its functional specifications. Our approach utilizes multi-material microstructures as low-level building blocks of the object. We start by precomputing the material property gamut—the set of bulk material properties that can be achieved with all material microstructures of a given size. We represent the boundary of this material property gamut using a level set field. Next, we propose an efficient and general topology optimization algorithm that simultaneously computes an optimal object topology and spatially varying material properties constrained by the precomputed gamut. Finally, we map the optimal spatially varying material properties onto the microstructures with the corresponding properties to generate a high-resolution printable structure. We demonstrate the efficacy of our framework by designing, optimizing, and fabricating objects in different material property spaces on the level of a trillion voxels, that is, several orders of magnitude higher than what can be achieved with current systems.

Revisiting MAC Forgeries, Weak Keys and Provable Security of Galois/Counter Mode of Operation

Galois/Counter Mode (GCM) is a block cipher mode of operation widely adopted in many practical applications and standards, such as IEEE 802.1AE and IPsec. We demonstrate that to construct successful forgeries of GCM-like polynomial-based MAC schemes, hash collisions are not necessarily required and any polynomials could be used in the attacks, which removes the restrictions of attacks previously proposed by Procter and Cid. Based on these new discoveries on forgery attacks, we show that all subsets with no less than two authentication keys are weak key classes, if the final block cipher masking is computed additively. In addition, by utilizing a special structure of GCM, we turn these forgery attacks into birthday attacks, which will significantly increase their success probabilities. Furthermore, we provide a method to fix GCM in order to avoid the security proof flaw discovered by Iwata, Ohashi and Minematsu. By applying the method, the security bounds of GCM can be improved by a factor of around 220. Lastly, we show that these forgery attacks will still succeed if GCM adopts MAC-then-Enc paradigm to protect its MAC scheme as one of the options mentioned in previous papers.