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Malicious homomorphic secret sharing with applications to DV-NIZK and more
Get PDFHomomorphic Secret Sharing (CRYPTO 2016) allows a secret to be shared among two or more parties in such a way that the parties can locally evaluate a class of functions on their shares. Homomorphic secret sharing (HSS) schemes and their underlying techniques have facilitated a wide range of applications. To account for the fact that parties generating or evaluating the shares might act maliciously, variants of HSS schemes that allow detection of such malicious behavior have been introduced. However, all prior approaches of malicious HSS that capture the class of circuits either crucially rely on a random oracle or require an non-reusable setup.
In this work, we initiate the study of malicious public-key 2-party HSS in the standard model with reusable setup, where any malicious behavior during share generation and share evaluation can be detected. Towards constructing malicious HSS, we introduce the notion of homomorphic secret sharing with robust linear reconstruction (RLR-HSS) and show that this notion readily implies malicious HSS. We outline challenges in instantiating RLR-HSS due to the error present in all current HSS constructions not relying on SHE/FHE, and show how to overcome these using derandomization techniques by Dwork et al. (EUROCRYPT 2004). Finally, we show applications of malicious HSS to compact designated verifier non-interactive zero knowledge arguments and maliciously secure 2-party computation in the standard model (supporting the same function class as the underlying malicious HSS)
Interior point methods are not worse than simplex
Get PDFWe develop a new "subspace layered least squares" interior point method (IPM) for solving linear programs. Applied to an -variable linear program in standard form, the iteration complexity of our IPM is up to an factor upper bounded by the straight-line complexity (SLC) of the linear program. This term refers to the minimum number of segments of any piecewise linear curve that traverses the wide neighborhood of the central path, a lower bound on the iteration complexity of any IPM that follows a piecewise linear trajectory along a path induced by a self-concordant barrier. In particular, our algorithm matches the number of iterations of any such IPM up to the same factor . As our second contribution, we show that the SLC of any linear program is upper bounded by , which implies that our IPM’s iteration complexity is at most exponential. This is in contrast to existing iteration complexity bounds that depend on either bit complexity or condition measures; these can be unbounded in the problem dimension. We achieve our upper bound by showing that the central path is well-approximated by a combinatorial proxy we call the max central path, which consists of shadow vertex simplex paths. Our upper bound complements the lower bounds of Allamigeon et al. [SIAM J. Appl. Algebra Geom., 2 (2018), pp. 140–178] and Allamigeon, Gaubert, and Vandame [No self-concordant barrier interior point method is strongly polynomial, 2022], who constructed linear programs with exponential SLC. Finally, we show that each iteration of our IPM can be implemented in strongly polynomial time. Along the way, we develop a deterministic algorithm that approximates the singular value decomposition of a matrix in strongly polynomial time to high accuracy, which may be of independent interest
A roadmap for responsible robotics: Promoting human agency and collaborative efforts
No full textThis document presents the outcomes of the Dagstuhl Seminar 'Roadmap for Responsible Robotics,' held in September 2023 at the Leibniz Center for Informatics, Schloss Dagstuhl, Germany. The seminar brought together researchers from the fields of robotics, computer science, social and cognitive sciences, and philosophy with the aim of charting a path toward improving responsibility in robotic systems. Through intensive interdisciplinary discussions centered on the various values at stake as robotics increasingly integrates into human life, the participants identified key priorities to guide future research and regulatory efforts. The resulting road map outlines actionable steps to ensure that robotic systems coevolve with human societies, promoting human agency and humane values rather than undermining them. Designed for diverse stakeholders - researchers, policy makers, industry leaders, practitioners, nongovernmental organizations (NGOs), and civil society groups - this road map provides a foundation for collaborative efforts toward responsible robotics
UVG-CWI-DQPC: Dual-quality point cloud dataset for volumetric video applications
Get PDFVolumetric video is a key enabler of immersive extended reality (XR) experiences and is often represented using point clouds for their structural simplicity. However, capturing volumetric content through multi-view acquisition and depth sensing poses many challenges, such as occlusions and depth mismatches. To foster research in this field, we introduce a unique dual-quality point cloud dataset, named UVG-CWI-DQPC, which is designed to support the development of point cloud enhancement, compression, and quality assessment. Our dataset includes 12 dynamic sequences captured simultaneously by: 1) a high-end capture system producing high-fidelity point clouds with extensive processing; and 2) a consumer-grade capture system relying on affordable RGB-D cameras, lightweight processing, and open-source tools. For each sequence, our dataset provides ground-truth point clouds from the high-end capture system and raw RGB-D footage from the consumer-grade capture system, along with calibration data and tools for point cloud generation. This dual-quality setup enables direct comparison and benchmarking of algorithms for densification, occlusion removal, registration, and quality enhancement. Our dataset is publicly available under a permissive license to support reproducible research and standardization work in Moving Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP)
The future of work is blended, not hybrid
Get PDFThe way we work is no longer hybrid—it is blended with AI co-workers, automated decisions, and virtual presence reshaping human roles, agency, and expertise. We now work through AI, with our outputs shaped by invisible algorithms. AI’s infiltration into knowledge, creative, and service work is not just about automation, but concerns redistribution of agency, creativity, and control. How do we deal with physical and distributed AI-mediated workspaces? What happens when algorithms co-author reports, and draft our creative work? In this provocation, we argue that hybrid work is obsolete. Blended work is the future, not just in physical and virtual spaces but in how human effort and AI output become inseparable. We argue this shift demands urgent attention to AI-mediated work practices, work-life boundaries, physical-digital interactions, and AI transparency and accountability. The question is not whether we accept it, but whether we actively shape it before it shapes us
Curating with technology: How to bring old fashion back to life in museum exhibitions
Get PDFSocial museums, constantly challenged by changing visitors' needs, are beginning to adopt technology in order to enrich guests' experiences. However, designing an exhibition that incorporates digital tools is not easy - it requires a new approach and expertise in both cultural heritage and technology. At the same time, there is a lack of clear guidance on how to effectively design digitally enhanced exhibitions. In this work we follow a human-centric approach, which engages both museum curators and technical experts throughout all stages of the exhibition design. The process, presented in Figure 1, starts with a focus group with curators (N = 4) aiming at understanding the current museum challenges and exploring ways to address them. Based on the workshop results, an initial design is prepared, which is later reiterated during 8 co-design sessions (N = 15). The final design is validated during the validation session (N = 6), resulting in a set of requirements important for social VR fashion exhibition design. The study provides insights for curators into how exhibitions of the future could look like and guidelines on how to design such an exhibition, engaging the technology team throughout the whole process
A survey on one-to-many negotiation: A taxonomy of interdependency
Get PDFOne-to-many negotiations are widely applied in various domains, contributing to efficient resource allocation and effective decision making. This wide variety of applications also brings a wide variety of implemented protocols, terminology and utility functions, which makes it hard to compare and improve strategies using existing solutions. We introduce a meta-model of negotiations, which characterizes almost all one-to-many negotiation research, bringing a unified description of the negotiations. This meta-model allows us to identify different classes of interdependency based on utility functions. We show how existing one-to-many negotiations are related to each other, finding new insights and identifying knowledge gaps. We suggest that a general utility function framework and benchmark scenarios for one-to-many negotiations could accommodate future advancement in this field
Subjective and objective quality assessment for Dynamic Point Cloud with visual attention in 6 DoF
No full textPerceptual quality assessment of Dynamic Point Cloud (DPC) contents plays an important role in various Virtual Reality (VR) applications that involve human beings as the end user. Understanding and modeling perceptual quality assessment is greatly enriched by insights from visual attention. However, incorporating aspects of visual attention in DPC quality models is largely unexplored, as ground-truth visual attention data are scarcely available. Besides, testing methods and procedures for collecting visual attention data are still to be agreed on. This article presents a dataset containing subjective opinion scores and visual attention maps of DPCs, collected in a VR environment using eye-tracking technology. Both the quality score and eye-tracking data were collected during a subjective quality assessment experiment, in which subjects were instructed to watch and rate DPCs at various degradation levels under 6 Degrees of Freedom (DoF) inspection, using a head-mounted display. Qualitative interview analysis was also conducted after the experiment. The dataset consists of 50 DPCs, including 5 reference DPCs, with each reference encoded at 3 distortion levels using 3 different codecs (namely G-PCC, V-PCC, CWI-PCL), amounting to a total of 9 degraded version per reference. Additionally, it incorporates 1,000 gaze trials from 40 participants, yielding a total of 15,000 visual attention maps across all the DPCs. We additionally benchmark objective quality metrics originally designed for static point clouds, evaluating their performance in our dataset using two temporal pooling strategies. Furthermore, we employ the visual attention data that are retrieved during our experiment to evaluate whether the performance of widely used objective quality metrics is improved by considering subjective measurements of visual attention. This dataset establishes a link between quality assessment and visual attention within the context of DPC. Moreover, thematic analysis of the interviews helps uncover user behavior and factors impacting perceptual quality for DPC in 6 DoF. This work deepens our understanding of DPC quality assessment and visual attention, driving progress in the realm of VR experiences and perception
Invariant control strategies for active flow control using graph neural networks
Get PDFReinforcement learning (RL) has recently gained traction for active flow control tasks, with initial applications exploring drag mitigation via flow field augmentation around a two-dimensional cylinder. RL has since been extended to more complex turbulent flows and has shown significant potential in learning complex control strategies. However, such applications remain computationally challenging owing to its sample inefficiency and associated simulation costs. This fact is worsened by the lack of generalization capabilities of these trained policy networks, often being implicitly tied to the input configurations of their training conditions. In this work, we propose the use of graph neural networks (GNNs) to address this particular limitation, effectively increasing the range of applicability and getting more value out of the upfront RL training cost. GNNs can naturally process unstructured, three-dimensional flow data, preserving spatial relationships without the constraints of a Cartesian grid. Additionally, they incorporate rotational, reflectional, and permutation invariance into the learned control policies, thus improving generalization and thereby removing the shortcomings of commonly used convolutional neural networks (CNNs) or multilayer perceptron (MLP) architectures. To demonstrate the effectiveness of this approach, we revisit the well-established two-dimensional cylinder benchmark problem for active flow control. The RL training is implemented using Relexi, a high-performance RL framework, with flow simulations conducted in parallel using the high-order discontinuous Galerkin framework FLEXI. Our results show that GNN-based control policies achieve comparable performance to existing methods while benefiting from improved generalization properties. This work establishes GNNs as a promising architecture for RL-based flow control and highlights the capabilities of Relexi and FLEXI for large-scale RL applications in fluid dynamics
