Doing Business in Mexico

[Excerpt] This memorandum provides a general summary of certain aspects of Mexican law, which may be of interest to foreign companies considering doing business in Mexico. The areas of law summarized in this memorandum include: 1. Foreign Investment Law; 2. Competition Law 3. Maquiladora Operations; 4. Company Law; 5. Taxes; 6. International Trade; 7. Labor Law; 8. Environmental; and 9. Intellectual Property. Treaties, to which Mexico is a party, particularly the North American Free Trade Agreement (the “NAFTA”) among Canada, Mexico and the United States, may affect investors from certain countries and may modify the preceding areas of Mexican law..… Although this memorandum makes numerous references to NAFTA and other treaties, it does not comprehensively address all instances in which Mexican law is modified or complemented thereby

A Family of Binary Sequences with Optimal Correlation Property and Large Linear Span

A family of binary sequences is presented and proved to have optimal correlation property and large linear span. It includes the small set of Kasami sequences, No sequence set and TN sequence set as special cases. An explicit lower bound expression on the linear span of sequences in the family is given. With suitable choices of parameters, it is proved that the family has exponentially larger linear spans than both No sequences and TN sequences. A class of ideal autocorrelation sequences is also constructed and proved to have large linear span.Comment: 21 page

A Hardware Oriented Method to Generate and Evaluate Nonlinear Interleaved Sequences with Desired properties

It is well known that the combinatorial structure, algebraic structure and D-transform based method render the nonlinear sequences with good autocorrelation function (ACF) and great linear complexity (LC). However, “all sequences” are not equal even if they are “born” by the same method! In this paper the big inequalities regarding LC of these sequences are shown based on a hardware oriented method (D-transform). In order to get the right sequences some more extensive simulations and trade off are needed. That is why this paper is represented here with above Title. Keywords: cryptography, mobile communications, security, watermarking, D-transfor

The combinatorics of binary arrays

This paper gives an account of the combinatorics of binary arrays, mainly concerning their randomness properties. In many cases the problem reduces to the investigation on difference sets.postprin

A note on low correlation zone signal sets

Abstract. In this note, we present a connection between designing low correlation zone (LCZ) sequences and the results of correlation of sequences with subfield decompositions presented in a recent book by the first two authors [2]. This results in low correlation zone signal sets with huge sizes over three different alphabetic sets: finite field of size q, integer residue ring modulo q, and the subset in the complex field which consists of powers of a primitive q-th root of unity. We also provide two open problems along this direction. Index Terms: low correlation zone sequences, subfield reducible sequences, two-tuple balance property.

XONN: XNOR-based Oblivious Deep Neural Network Inference

Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge in this setting is to ensure the privacy of the clients' sensitive data. Oblivious inference is the task of running the neural network on the client's input without disclosing the input or the result to the server. This paper introduces XONN, a novel end-to-end framework based on Yao's Garbled Circuits (GC) protocol, that provides a paradigm shift in the conceptual and practical realization of oblivious inference. In XONN, the costly matrix-multiplication operations of the deep learning model are replaced with XNOR operations that are essentially free in GC. We further provide a novel algorithm that customizes the neural network such that the runtime of the GC protocol is minimized without sacrificing the inference accuracy. We design a user-friendly high-level API for XONN, allowing expression of the deep learning model architecture in an unprecedented level of abstraction. Extensive proof-of-concept evaluation on various neural network architectures demonstrates that XONN outperforms prior art such as Gazelle (USENIX Security'18) by up to 7x, MiniONN (ACM CCS'17) by 93x, and SecureML (IEEE S&P'17) by 37x. State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number of layers in the model. XONN is first to perform oblivious inference on Fitnet architectures with up to 21 layers, suggesting a new level of scalability compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to perform privacy-preserving medical diagnosis.Comment: To appear in USENIX Security 201