MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture

Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results: - any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs; - assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - ? fraction of machines (for an arbitrarily small constant ?) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup. As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques

The role of subjective knowledge and perceived trustworthiness in fair trade consumption for fashion and food products

Purpose: The purpose of this study is to examine how subjective knowledge about fair trade products and the perceived trustworthiness of information about fair trade goods influence purchase intention and reported purchase behaviour across two product categories, namely, fashion and food. Design/methodology/approach: Data were collected from an online survey with a sample of 1,616 consumers in four European countries, namely, Germany, Italy, Austria and the UK. Findings: The results show that subjective knowledge moderates the positive relationship between intentions to purchase and reported purchase behaviour of fair trade products, however, the moderating role of perceived information trustworthiness was not significant. Furthermore, both the intention to purchase and reported purchase behaviour are significantly lower for fair trade fashion products than for fair trade food products. Practical implications: This paper shows how fair trade consumption behaviour is mainly influenced by subjective knowledge about fair trade products. It reveals existing differences in both the buying intentions and reported purchase behaviour in different European markets. Originality/value: This research broadens the understanding of consumers’ fair trade consumption behaviour across two different product categories and four different countries, with a focus on the interaction effect of consumers’ subjective knowledge and information trustworthiness

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.Comment: 3DV 201

Asymmetric magnetization splitting in diamond domain structure: Dependence on exchange interaction and anisotropy

The distributions of magnetization orientation for both Landau and diamond domain structures in nano-rectangles have been investigated by micromagnetic simulation with various exchange coefficient and anisotropy constant. Both symmetric and asymmetric magnetization splitting are found in diamond domain structure, as well as only symmetric magnetization splitting in Landau structure. In the Landau structure, the splitting angle increases with the exchange coefficient but decreases slightly with the anisotropy constant, suggesting that the exchange interaction mainly contributes to the magnetization splitting in Landau structure. However in the diamond structure, the splitting angle increases with the anisotropy constant but derceases with the exchange coefficient, indicating that the magnetization splitting in diamond structure is resulted from magnetic anisotropy.Comment: 5 pages, 5 figure