On the Explanation and Implementation of Three Open-Source Fully Homomorphic Encryption Libraries

While fully homomorphic encryption (FHE) is a fairly new realm of cryptography, it has shown to be a promising mode of information protection as it allows arbitrary computations on encrypted data. The development of a practical FHE scheme would enable the development of secure cloud computation over sensitive data, which is a much-needed technology in today\u27s trend of outsourced computation and storage. The first FHE scheme was proposed by Craig Gentry in 2009, and although it was not a practical implementation, his scheme laid the groundwork for many schemes that exist today. One main focus in FHE research is the creation of a library that allows users without much knowledge of the complexities of FHE to use the technology securely. In this paper, we will present the concepts behind FHE, together with the introduction of three open-source FHE libraries, in order to bring better understanding to how the libraries function

HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks

While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, which leaves many avenues for an adversary to exploit. In this work, we propose an efficient and robust training approach to defend against data poisoning attacks based on influence functions, named Healthy Influential-Noise based Training. Using influence functions, we craft healthy noise that helps to harden the classification model against poisoning attacks without significantly affecting the generalization ability on test data. In addition, our method can perform effectively when only a subset of the training data is modified, instead of the current method of adding noise to all examples that has been used in several previous works. We conduct comprehensive evaluations over two image datasets with state-of-the-art poisoning attacks under different realistic attack scenarios. Our empirical results show that HINT can efficiently protect deep learning models against the effect of both untargeted and targeted poisoning attacks

Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions

How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate the ability of influence functions to offer insight into how a specific privacy parameter value will affect a model's test loss in the randomized response-based local DP setting. Our proposed method allows a data curator to select the privacy parameter best aligned with their allowed privacy-utility trade-off without requiring heavy computation such as extensive model retraining and data privatization. We consider multiple common randomization scenarios, such as performing randomized response over the features, and/or over the labels, as well as the more complex case of applying a class-dependent label noise correction method to offset the noise incurred by randomization. Further, we provide a detailed discussion over the computational complexity of our proposed approach inclusive of an empirical analysis. Through empirical evaluations we show that for both binary and multi-class settings, influence functions are able to approximate the true change in test loss that occurs when randomized response is applied over features and/or labels with small mean absolute error, especially in cases where noise correction methods are applied.Comment: 11 pages, 2 figure

Protoplanetary Disk Science Enabled by Extremely Large Telescopes

The processes that transform gas and dust in circumstellar disks into diverse exoplanets remain poorly understood. One key pathway is to study exoplanets as they form in their young (∼few~Myr) natal disks. Extremely Large Telescopes (ELTs) such as GMT, TMT, or ELT, can be used to establish the initial chemical conditions, locations, and timescales of planet formation, via (1)~measuring the physical and chemical conditions in protoplanetary disks using infrared spectroscopy and (2)~studying planet-disk interactions using imaging and spectro-astrometry. Our current knowledge is based on a limited sample of targets, representing the brightest, most extreme cases, and thus almost certainly represents an incomplete understanding. ELTs will play a transformational role in this arena, thanks to the high spatial and spectral resolution data they will deliver. We recommend a key science program to conduct a volume-limited survey of high-resolution spectroscopy and high-contrast imaging of the nearest protoplanetary disks that would result in an unbiased, holistic picture of planet formation as it occurs