Homomorphic Encryption for Multiple Users with Less Communications

Keeping privacy for every entity in outsourced computation is always a crucial issue. For efficient secure computation, homomorphic encryption (HE) can be one of nice solutions. Especially, multikey homomorphic encryption (MKHE) which allows homomorphic evaluation on encrypted data under different keys can be one of the simplest solutions for a secure computation which handles multiple users\u27 data. However, the current main problem of MKHE is that the dimension of its evaluated ciphertext relies on the number of users. To solve this problem, there are several variants of multikey homomorphic encryption schemes to keep the size of ciphertext constant for a fixed number of users. However, users interact one another before computation to provide their inputs, which increases setup complexity. Moreover, all the existing MKHE schemes and their variants have unique benefits which cannot be easily achieved at the same time in one scheme. In other words, each type of scheme has a suitable computational scenario to put its best performance. In this paper, we suggest more efficient evaluation key generation algorithms (relinearization key and bootstrapping key) for the existing variants of MKHE schemes which have no ciphertext expansion for a fixed number of users. Our method only requires a very simple and minor pre-processing; distributing public keys, which is not counted as a round at all in many other applications. Regarding bootstrapping, we firstly provide an efficient bootstrapping for multiple users which is the same as the base single-key scheme thanks to our simplified key generation method without a communication. As a result, participants have less communication, computation, and memory cost in online phase. Moreover, we provide a practical conversion algorithm between the two types of schemes in order to \emph{efficiently} utilize both schemes\u27 advantages together in more various applications. We also provide detailed comparison among similar results so that users can choose a suitable scheme for their homomorphic encryption based application scenarios

SPOTS: Stable Placement of Objects with Reasoning in Semi-Autonomous Teleoperation Systems

Pick-and-place is one of the fundamental tasks in robotics research. However, the attention has been mostly focused on the ``pick'' task, leaving the ``place'' task relatively unexplored. In this paper, we address the problem of placing objects in the context of a teleoperation framework. Particularly, we focus on two aspects of the place task: stability robustness and contextual reasonableness of object placements. Our proposed method combines simulation-driven physical stability verification via real-to-sim and the semantic reasoning capability of large language models. In other words, given place context information (e.g., user preferences, object to place, and current scene information), our proposed method outputs a probability distribution over the possible placement candidates, considering the robustness and reasonableness of the place task. Our proposed method is extensively evaluated in two simulation and one real world environments and we show that our method can greatly increase the physical plausibility of the placement as well as contextual soundness while considering user preferences.Comment: 7 page

SOCRATES: Text-based Human Search and Approach using a Robot Dog

In this paper, we propose a SOCratic model for Robots Approaching humans based on TExt System (SOCRATES) focusing on the human search and approach based on free-form textual description; the robot first searches for the target user, then the robot proceeds to approach in a human-friendly manner. In particular, textual descriptions are composed of appearance (e.g., wearing white shirts with black hair) and location clues (e.g., is a student who works with robots). We initially present a Human Search Socratic Model that connects large pre-trained models in the language domain to solve the downstream task, which is searching for the target person based on textual descriptions. Then, we propose a hybrid learning-based framework for generating target-cordial robotic motion to approach a person, consisting of a learning-from-demonstration module and a knowledge distillation module. We validate the proposed searching module via simulation using a virtual mobile robot as well as through real-world experiments involving participants and the Boston Dynamics Spot robot. Furthermore, we analyze the properties of the proposed approaching framework with human participants based on the Robotic Social Attributes Scale (RoSAS)Comment: Project page: https://socratesrobotdog.github.io

CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents

In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e., clear) or not (i.e., ambiguous or infeasible). Once the command is classified as uncertain, we further distinguish it between ambiguous or infeasible commands leveraging LLMs with situational aware context in a zero-shot manner. For ambiguous commands, we disambiguate the command by interacting with users via question generation with LLMs. We believe that proper recognition of the given commands could lead to a decrease in malfunction and undesired actions of the robot, enhancing the reliability of interactive robot agents. We present a dataset for robotic situational awareness, consisting pair of high-level commands, scene descriptions, and labels of command type (i.e., clear, ambiguous, or infeasible). We validate the proposed method on the collected dataset, pick-and-place tabletop simulation. Finally, we demonstrate the proposed approach in real-world human-robot interaction experiments, i.e., handover scenarios

Efficient TFHE Bootstrapping in the Multiparty Setting

In this paper, we introduce a new approach to efficiently compute TFHE bootstrapping keys for (predefined) multiple users. Hence, a fixed number of users can enjoy the same level of efficiency as in the single key setting, keeping their individual input privacy. Our construction relies on a novel algorithm called homomorphic indicator, which can be of independent interest. We provide a detailed analysis of the noise growth and a set of secure parameters suitable to be used in practice. Moreover, we compare the complexity of our technique with other state-of-the-art constructions and show which method performs better in what parameter sets, based on our noise analysis. We also provide a prototype implementation of our technique. To the best of our knowledge, this is the first implementation of TFHE in the multiparty setting

Active Visual Search in the Wild

In this paper, we focus on the problem of efficiently locating a target object described with free-form language using a mobile robot equipped with vision sensors (e.g., an RGBD camera). Conventional active visual search predefines a set of objects to search for, rendering these techniques restrictive in practice. To provide added flexibility in active visual searching, we propose a system where a user can enter target commands using free-form language; we call this system Active Visual Search in the Wild (AVSW). AVSW detects and plans to search for a target object inputted by a user through a semantic grid map represented by static landmarks (e.g., desk or bed). For efficient planning of object search patterns, AVSW considers commonsense knowledge-based co-occurrence and predictive uncertainty while deciding which landmarks to visit first. We validate the proposed method with respect to SR (success rate) and SPL (success weighted by path length) in both simulated and real-world environments. The proposed method outperforms previous methods in terms of SPL in simulated scenarios with an average gap of 0.283. We further demonstrate AVSW with a Pioneer-3AT robot in real-world studies

Hydrodynamic Study on the “Stop-and-Acceleration” Pattern of Refilling Flow at Perforation Plates by Using a Xylem-Inspired Channel

Porous structures, such as perforation plates and pit membranes, have attracted considerable attention due to their hydraulic regulation of water flow through vascular plant networks. However, limited information is available regarding the hydraulic functions of such structures during water-refilling and embolism repair because of difficulties in simultaneous in vivo measurements of refilling flow and pressure variations in xylem vessels. In this study, we developed a xylem-inspired microchannel with a porous mesh for systematic investigation on the hydraulic contribution of perforation plates on water-refilling. In particular, the “stop-and-acceleration” phenomenon of the water meniscus at the porous mesh structure was carefully examined in macroscopic and microscopic views. This distinctive phenomenon usually occurs in the xylem vessels of vascular plants during embolism repair. Based on the experimental results, we established a theoretical model of the flow characteristics and pressure variations around the porous structure inside the microchannel. Perforation plates could be speculated to be a pressure-modulated flow controller that facilitates embolism recovery. Furthermore, the proposed xylem-inspired channel can be used to investigate the hydraulic functions of porous structures for water management in plants

Practical Randomized Lattice Gadget Decomposition With Application to FHE

Gadget decomposition is widely used in lattice based cryptography, especially homomorphic encryption (HE) to keep the noise growth slow. If it is randomized following a subgaussian distribution, it is called subgaussian (gadget) decomposition which guarantees that we can bound the noise contained in ciphertexts by its variance. This gives tighter and cleaner noise bound in average case, instead of the use of its norm. Even though there are few attempts to build efficient such algorithms, most of them are still not practical enough to be applied to homomorphic encryption schemes due to somewhat high overhead compared to the deterministic decomposition. Furthermore, there has been no detailed analysis of existing works. Therefore, HE schemes use the deterministic decomposition algorithm and rely on a Heuristic assumption that every output element follows a subgaussian distribution independently. In this work, we introduce a new practical subgaussian gadget decomposition algorithm which has the least overhead (less than 14\%) among existing works for certain parameter sets, by combining two previous works. In other words, we bring an existing technique based on an uniform distribution to a simpler and faster design (PKC\u27 22) to exploit parallel computation, which allows to skip expensive parts due to pre-computation, resulting in even simpler and faster algorithm. When the modulus is large (over 100-bit), our algorithm is not always faster than the other similar work. Therefore, we give a detailed comparison, even for large modulus, with all the competitive algorithms for applications to choose the best algorithm for their choice of parameters