Implications of Higgs Searches on the Four Generation Standard Model

Within the four generation Standard Model, the Higgs couplings to gluons and to photons deviate in a significant way from the predictions of the three generation Standard Model. As a consequence, large departures in several Higgs production and decay channels are expected. Recent Higgs search results, presented by ATLAS, CMS and CDF, hint on the existence of a Higgs boson with a mass around 125 GeV. Using these results and assuming such a Higgs boson, we derive exclusion limits on the four generation Standard Model. For m_H = 125 GeV, the model is excluded at 99.9% confidence level. For 124 GeV <= m_H <= 127 GeV, an exclusion limit above 95% confidence level is found.Comment: 4 pages, 2 figures; v2: updated LHC results, refined sca

Bootstrapping Homomorphic Encryption via Functional Encryption

Homomorphic encryption is a central object in modern cryptography, with far-reaching applications. Constructions supporting homomorphic evaluation of arbitrary Boolean circuits have been known for over a decade, based on standard lattice assumptions. However, these constructions are leveled, meaning that they only support circuits up to some a-priori bounded depth. These leveled constructions can be bootstrapped into fully homomorphic ones, but this requires additional circular security assumptions, which are construction-dependent, and where reductions to standard lattice assumptions are no longer known. Alternative constructions are known based on indistinguishability obfuscation, which has been recently constructed under standard assumptions. However, this alternative requires subexponential hardness of the underlying primitives. We prove a new bootstrapping theorem based on functional encryption, which is known based on standard polynomial hardness assumptions. As a result we obtain the first fully homomorphic encryption scheme that avoids both circular security assumptions and super-polynomial hardness assumptions. The construction is secure against uniform adversaries, and can be made non-uniformly secure assuming a generalization of the time-hierarchy theorem, which follows for example from non-uniform ETH. At the heart of the construction is a new proof technique based on cryptographic puzzles and decomposable obfuscation. Unlike most cryptographic reductions, our security reduction does not fully treat the adversary as a black box, but rather makes explicit use of its running time (or circuit size)

Timing leakage analysis of non-constant-time NTT implementations with Harvey butterflies

Harvey butterflies and their variants are core primitives in many optimized number-theoretic transform (NTT) implementations, such as those used by the HElib and SEAL homomorphic encryption libraries. However, these butterflies are not constant-time algorithms and may leak secret data when incorrectly implemented. Luckily for SEAL and HElib, the compilers optimize the code to run in constant-time. We claim that relying on the compiler is risky and demonstrate how a simple code modification can cause leakage, which can reduce the hardness of the ring learning with errors (R-LWE) instances used by these libraries, for example, from 2^128 to 2^104

Online Meta-Learning For Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.Comment: arXiv admin note: text overlap with arXiv:2103.1348

BLEACH: Cleaning Errors in Discrete Computations over CKKS

Approximated homomorphic encryption (HE) schemes such as CKKS are commonly used to perform computations over encrypted real numbers. It is commonly assumed that these schemes are not “exact” and thus they cannot execute circuits with unbounded depth over discrete sets, such as binary or integer numbers, without error overflows. These circuits are usually executed using BGV and B/FV for integers and TFHE for binary numbers. This artificial separation can cause users to favor one scheme over another for a given computation, without even exploring other, perhaps better, options. We show that by treating step functions as “clean-up” utilities and by leveraging the SIMD capabilities of CKKS, we can extend the homomorphic encryption toolbox with efficient tools. These tools use CKKS to run unbounded circuits that operate over binary and small-integer elements and even combine these circuits with fixed-point real numbers circuits. We demonstrate the results using the Turing-complete Conway’s Game of Life. In our evaluation, for boards of size 128x128, these tools achieved an order of magnitude faster latency than previous implementations using other HE schemes. We argue and demonstrate that for large enough real-world inputs, performing binary circuits over CKKS, while considering it as an “exact” scheme, results in comparable or even better performance than using other schemes tailored for similar inputs