Reviewerly/exHarmony huggingface dataset:
Batch Bootstrapping II:

This work continues the exploration of the batch framework proposed in Batch Bootstrapping I (Liu and Wang, Eurocrypt 2023). By further designing novel batch homomorphic algorithms based on the batch framework, this work shows how to bootstrap $$\lambda $$ LWE input ciphertexts within a polynomial modulus, using $$\tilde{O}(\lambda )$$ FHE multiplications. This implies an amortized complexity $$\tilde{O}(1)$$ FHE multiplications per input ciphertext, significantly improving our first work (whose amortized complexity is $$\tilde{O}(\lambda ^{0.75})$$ ) and the theoretical state of the art MS18 (Micciancio and Sorrell, ICALP 2018), whose amortized complexity is $$O(3^{1/\epsilon } \cdot \lambda ^{\epsilon })$$ , for any arbitrary constant $$\epsilon $$ . We believe that all our new homomorphic algorithms might be useful in general applications, and thus can be of independent interests.
Fully Adaptive Decentralized Multi-Authority ABE

Decentralized multi-authority attribute-based encryption ( $$\textsf{MA}\text {-}\textsf{ABE}$$ ) is a distributed generalization of standard (ciphertext-policy) attribute-based encryption where there is no trusted central authority: any party can become an authority and issue private keys, and there is no requirement for any global coordination other than the creation of an initial set of common reference parameters. We present the first multi-authority attribute-based encryption schemes that are provably fully-adaptively secure. Namely, our construction is secure against an attacker that may corrupt some of the authorities as well as perform key queries adaptively throughout the life-time of the system. Our main construction relies on a prime order bilinear group where the k-linear assumption holds as well as on a random oracle. Along the way, we present a conceptually simpler construction relying on a composite order bilinear group with standard subgroup decision assumptions as well as on a random oracle. Prior to this work, there was no construction that could resist adaptive corruptions of authorities, no matter the assumptions used. In fact, we point out that even standard complexity leveraging style arguments do not work in the multi-authority setting.
Batch Bootstrapping I:

In this series of work, we aim at improving the bootstrapping paradigm for fully homomorphic encryption (FHE). Our main goal is to show that the amortized cost of bootstrapping within a polynomial modulus only requires $$\tilde{O}(1)$$ FHE multiplications. To achieve this, we develop substantial algebraic techniques in two papers. Particularly, the first one (this work) proposes a new mathematical framework for batch homomorphic computation that is compatible with the existing bootstrapping methods of AP14/FHEW/TFHE. To show that our overall method requires only a polynomial modulus, we develop a critical algebraic analysis over noise growth, which might be of independent interest. Overall, the framework yields an amortized complexity $$\tilde{O}(\lambda ^{0.75})$$ FHE multiplications, where $$\lambda $$ is the security parameter. This improves the prior methods of AP14/FHEW/TFHE, which required $$O(\lambda )$$ FHE multiplications in amortization. Developing many substantial new techniques based on the foundation of this work, the sequel (Bootstrapping II, Eurocrypt 2023) shows how to further improve the recursive bootstrapping method of MS18 (Micciancio and Sorrell, ICALP 2018), yielding a substantial theoretical improvement that can potentially lead to more practical methods.
Functional Commitments for All Functions, with Transparent Setup and from SIS

A functional commitment scheme enables a user to concisely commit to a function from a specified family, then later concisely and verifiably reveal values of the function at desired inputs. Useful special cases, which have seen applications across cryptography, include vector commitments and polynomial commitments. To date, functional commitments have been constructed (under falsifiable assumptions) only for functions that are essentially linear, with one recent exception that works for arbitrarily complex functions. However, that scheme operates in a strong and non-standard model, requiring an online, trusted authority to generate special keys for any opened function inputs. In this work, we give the first functional commitment scheme for nonlinear functions—indeed, for all functions of any bounded complexity—under a standard setup and a falsifiable assumption. Specifically, the setup is "transparent," requiring only public randomness (and not any trusted entity), and the assumption is the hardness of the standard Short Integer Solution (SIS) lattice problem. Our construction also has other attractive features, including: stateless updates via generic composability; excellent asymptotic efficiency for the verifier, and also for the committer in important special cases like vector and polynomial commitments, via preprocessing; and post-quantum security, since it is based on SIS.
HyperPlonk: Plonk with Linear-Time Prover and High-Degree Custom Gates

Plonk is a widely used succinct non-interactive proof system that uses univariate polynomial commitments. Plonk is quite flexible: it supports circuits with low-degree "custom" gates as well as circuits with lookup gates (a lookup gate ensures that its input is contained in a predefined table). For large circuits, the bottleneck in generating a Plonk proof is the need for computing a large FFT. We present HyperPlonk, an adaptation of Plonk to the boolean hypercube, using multilinear polynomial commitments. HyperPlonk retains the flexibility of Plonk but provides several additional benefits. First, it avoids the need for an FFT during proof generation. Second, and more importantly, it supports custom gates of much higher degree than Plonk without harming the running time of the prover. Both of these can dramatically speed up the prover's running time. Since HyperPlonk relies on multilinear polynomial commitments, we revisit two elegant constructions: one from Orion and one from Virgo. We show how to reduce the Orion opening proof size to less than 10 KB (an almost factor 1000 improvement) and show how to make the Virgo FRI-based opening proof simpler and shorter (This is an extended abstract. The full version is available on EPRINT[22]).
Chopsticks: Fork-Free Two-Round Multi-signatures from Non-interactive Assumptions

Multi-signatures have been drawing lots of attention in recent years, due to their applications in cryptocurrencies. Most early constructions require three-round signing, and recent constructions have managed to reduce the round complexity to two. However, their security proofs are mostly based on non-standard, interactive assumptions (e.g. one-more assumptions) and come with a huge security loss, due to multiple uses of rewinding (aka the Forking Lemma). This renders the quantitative guarantees given by the security proof useless. In this work, we improve the state of the art by proposing two efficient two-round multi-signature schemes from the (standard, non-interactive) Decisional Diffie-Hellman (DDH) assumption. Both schemes are proven secure in the random oracle model without rewinding. We do not require any pairing either. Our first scheme supports key aggregation but has a security loss linear in the number of signing queries, and our second scheme is the first tightly secure construction. A key ingredient in our constructions is a new homomorphic dual-mode commitment scheme for group elements, that allows to equivocate for messages of a certain structure. The definition and efficient construction of this commitment scheme is of independent interest.
Unique-Path Identity Based Encryption with Applications to Strongly Secure Messaging

Hierarchical Identity Based Encryption (HIBE) is a well studied, versatile tool used in many cryptographic protocols. Yet, since the performance of all known HIBE constructions is broadly considered prohibitive, some real-world applications avoid relying on HIBE at the expense of security. A prominent example for this is secure messaging: Strongly secure messaging protocols are provably equivalent to Key-Updatable Key Encapsulation Mechanisms (KU-KEMs; Balli et al., Asiacrypt 2020); so far, all KU-KEM constructions rely on adaptive unbounded-depth HIBE (Poettering and Rösler, Jaeger and Stepanovs, both CRYPTO 2018). By weakening security requirements for better efficiency, many messaging protocols dispense with using HIBE. In this work, we aim to gain better efficiency without sacrificing security. For this, we observe that applications like messaging only need a restricted variant of HIBE for strong security. This variant, that we call Unique-Path Identity Based Encryption (UPIBE), restricts HIBE by requiring that each secret key can delegate at most one subordinate secret key. However, in contrast to fixed secret key delegation in Forward-Secure Public Key Encryption, the delegation in UPIBE, as in HIBE, is uniquely determined by variable identity strings from an exponentially large space. We investigate this mild but surprisingly effective restriction and show that it offers substantial complexity and performance advantages. More concretely, we generically build bounded-depth UPIBE from only bounded-collusion IBE in the standard model; and we generically build adaptive unbounded-depth UPIBE from only selective bounded-depth HIBE in the random oracle model. These results significantly extend the range of underlying assumptions and efficient instantiations. We conclude with a rigorous performance evaluation of our UPIBE design. Beyond solving challenging open problems by reducing complexity and improving efficiency of KU-KEM and strongly secure messaging protocols, we offer a new definitional perspective on the bounded-collusion setting.
Sliding into My DMs: Detecting Uncomfortable or Unsafe Sexual Risk Experiences within Instagram Direct Messages Grounded in the Perspective of Youth

We collected Instagram data from 150 adolescents (ages 13-21) that included 15,547 private message conversations of which 326 conversations were flagged as sexually risky by participants. Based on this data, we leveraged a human-centered machine learning approach to create sexual risk detection classifiers for youth social media conversations. Our Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level (AUC=0.88), and CNN outperformed at the message-level (AUC=0.85). We also trained classifiers to detect the severity risk level (i.e., safe, low, medium-high) of a given message with CNN outperforming other models (AUC=0.88). A feature analysis yielded deeper insights into patterns found within sexually safe versus unsafe conversations. We found that contextual features (e.g., age, gender, and relationship type) and Linguistic Inquiry and Word Count (LIWC) contributed the most for accurately detecting sexual conversations that made youth feel uncomfortable or unsafe. Our analysis provides insights into the important factors and contextual features that enhance automated detection of sexual risks within youths' private conversations. As such, we make valuable contributions to the computational risk detection and adolescent online safety literature through our human-centered approach of collecting and ground truth coding private social media conversations of youth for the purpose of risk classification.
Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust

Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency, and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive end-users to gain their trust. Participants interacted with two AI-based chess engines, trustworthy (competent, fair) and untrustworthy (incompetent, unfair), that coached participants by suggesting chess moves in three games against another engine opponent. We varied coaches' transparency about their competence (with the untrustworthy one always exaggerating its competence). We quantified and objectively measured participants' trust based on how often participants relied on coaches' move recommendations. Participants showed inability to assess AI competence by misplacing their trust with the untrustworthy AI, confirming its ability to deceive. Our work calls for design of interactions to help end-users assess AI trustworthiness.
Understanding Safety Risks and Safety Design in Social VR Environments

Understanding emerging safety risks in nuanced social VR spaces and how existing safety features are used is crucial for the future development of safe and inclusive 3D social worlds. Prior research on safety risks in social VR is mainly based on interview or survey data about social VR users' experiences and opinions, which lacks "in-situ observations" of how individuals react to these risks. Using two empirical studies, this paper seeks to understand safety risks and safety design in social VR. In Study 1, we investigated 212 YouTube videos and their transcripts that document social VR users' immediate experiences of safety risks as victims, attackers, or bystanders. We also analyzed spectators' reactions to these risks shown in comments to the videos. In Study 2, we summarized 13 safety features across various social VR platforms and mapped how each existing safety feature in social VR can mitigate the risks identified in Study 1. Based on the uniqueness of social VR interaction dynamics and users' multi-modal simulated reactions, we call for further re-thinking and re-approaching safety designs for future social VR environments and propose potential design implications for future safety protection mechanisms in social VR.
FSNet: Focus Scanning Network for Camouflaged Object Detection

Camouflaged object detection (COD) aims to discover objects that blend in with the background due to similar colors or textures, etc. Existing deep learning methods do not systematically illustrate the key tasks in COD, which seriously hinders the improvement of its performance. In this paper, we introduce the concept of focus areas that represent some regions containing discernable colors or textures, and develop a two-stage focus scanning network for camouflaged object detection. Specifically, a novel encoder-decoder module is first designed to determine a region where the focus areas may appear. In this process, a multi-layer Swin transformer is deployed to encode global context information between the object and the background, and a novel cross-connection decoder is proposed to fuse cross-layer textures or semantics. Then, we utilize the multi-scale dilated convolution to obtain discriminative features with different scales in focus areas. Meanwhile, the dynamic difficulty aware loss is designed to guide the network paying more attention to structural details. Extensive experimental results on the benchmarks, including CAMO, CHAMELEON, COD10K, and NC4K, illustrate that the proposed method performs favorably against other state-of-the-art methods.
On an Assmus–Mattson type theorem for type I and even formally self‐dual codes

Abstract In the present paper, we give an Assmus–Mattson type theorem for near‐extremal Type I and even formally self‐dual codes. We show the existence of 1‐designs or 2‐designs for these codes. As a corollary, we prove the uniqueness of a self‐orthogonal 2‐ design.
Enhancing Large Language Models with Climate Resources

Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability to generate human-like text across diverse topics. However, despite their impressive capabilities, LLMs lack recent information and often employ imprecise language, which can be detrimental in domains where accuracy is crucial, such as climate change. In this study, we make use of recent ideas to harness the potential of LLMs by viewing them as agents that access multiple sources, including databases containing recent and precise information about organizations, institutions, and companies. We demonstrate the effectiveness of our method through a prototype agent that retrieves emission data from ClimateWatch (https://www.climatewatchdata.org/) and leverages general Google search. By integrating these resources with LLMs, our approach overcomes the limitations associated with imprecise language and delivers more reliable and accurate information in the critical domain of climate change. This work paves the way for future advancements in LLMs and their application in domains where precision is of paramount importance.
Shapley Values for Explaining the Black Box Nature of Machine Learning Model Clustering

Machine learning (ML) models are becoming increasingly complex. In fact, a sophisticated model (XGBoost boosting or deep learning) generally leads to more accurate predictions than a simple model (linear regression or decision tree). There is therefore a trade-off between the performance of a model and its interpretability: what a model gains in performance, it loses in interpretability (and vice versa), where interpretability is the ability for a human to understand the reasons for a model's decision. However, explaining the predictions made by machine learning models aims at computing and interpreting the importance of features. To achieve this, game theory has recently gained attention for better understanding the similarity between group members. In this paper, we use SHAP (SHapley Additive exPlanations), which is a method based on cooperative game theory, to analyze and evaluate the properties of each group. More importantly, we rely k-means PCA and Light gbm classifier to improve the data preparation before grouping the features into multiple clusters. The simulation results prove the importance of shapley value in creating an accurate and meaningful representation of each cluster.
Analysis of Appeal for Realistic AI-Generated Photos

AI-generated images have gained in popularity in recent years due to improvements and developments in the field of artificial intelligence.This has led to several new AI generators, which may produce realistic, funny, and impressive images using a simple text prompt.DALL-E-2, Midjourney, and Craiyon are a few examples of the mentioned approaches.In general, it can be seen that the quality, realism, and appeal of the images vary depending on the used approach.Therefore, in this paper, we analyse to what extent such AI-generated images are realistic or of high appeal from a more photographic point of view and how users perceive them.To evaluate the appeal of several state-of-the-art AI generators, we develop a dataset consisting of 27 different text prompts, with some of them being based on the DrawBench prompts.Using these prompts we generated a total of 135 images with five different AI-Text-To-Image generators.These images in combination with real photos form the basis of our evaluation.The evaluation is based on an online subjective study and the results are compared with state-of-the-art image quality models and features.The results indicate that some of the included generators are able to produce realistic and highly appealing images.However, this depends on the approach and text prompt to a large extent.The dataset and evaluation of this paper are made publicly available for reproducibility, following an Open Science approach.
Remote Sensing Object Detection Based on Convolution and Swin Transformer

Remote sensing object detection is an essential task for surveying the earth.It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images.In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Transformer) algorithm is proposed to address the problems of remote sensing object detection, such as significant differences in target scales, complex backgrounds, and tightly arranged small-size targets.To increase the information interaction range of the feature map, make full use of the background information of the object, and improve the detection accuracy of the object with a complex background, the Regin Attention (RA) mechanism combined with Swin Transformer as the backbone is proposed to extract features.To improve the detection accuracy of small objects, the C3D module is used to fuse deep and shallow semantic information and optimize the multi-scale problem of remote sensing targets.To evaluate the performance of RAST-YOLO, extensive experiments are performed on DIOR and TGRS-HRRSD datasets.The experimental results show that RAST achieves state-of-the-art detection accuracy with high efficiency and robustness.Specifically, compared with the baseline network, the mean average precision (mAP) of detection results is improved by 5% and 2.3% on DIOR and TGRS-HRRSD datasets, respectively, which demonstrates RAST-YOLO is effective and superior.Moreover, the lightweight structure of RAST-YOLO can ensure the real-time detection speed and obtain excellent detection results.
Information Security Threats and Working from Home Culture: Taxonomy, Risk Assessment and Solutions

During the COVID-19 pandemic, most organizations were forced to implement a work-from-home policy, and in many cases, employees have not been expected to return to the office on a full-time basis. This sudden shift in the work culture was accompanied by an increase in the number of information security-related threats which organizations were unprepared for. The ability to effectively address these threats relies on a comprehensive threat analysis and risk assessment and the creation of relevant asset and threat taxonomies for the new work-from-home culture. In response to this need, we built the required taxonomies and performed a thorough analysis of the threats associated with this new work culture. In this paper, we present our taxonomies and the results of our analysis. We also examine the impact of each threat, indicate when it is expected to occur, describe the various prevention methods available commercially or proposed in academic research, and present specific use cases.
Web Content Management Systems as a Support Service in Academic Library Websites: An Investigation of the World-class Universities in 2012–2022

This study aims to describe and analyze the global status and trends in the application of Web Content Management Systems (WCMS) in academic library websites over the last decade, as reflected on the Internet Archive and current library websites. The study selected the top 1000 academic library websites listed in the 2023 QS World University Rankings, and collected and examined their WCMS adopted in 2012 and 2022 using Python software and the BuiltWith tool. Multiple visits were made to capture the distribution of WCMS, popular software, and software categories. The survey results indicated a significant change in the application of WCMS to academic library websites from 2012 to 2022. The number of libraries using WCMS increased by 288, and over half (61.9 %) of libraries adopted WCMS in 2022. Over 70 types of WCMS existed within academic libraries, while a small number of WCMS own the vast majority of the market share, with Drupal consistently being the most popular software. Significant geographical differences existed in the WCMS used by academic libraries in different countries/regions. The innovation theory and functional modules of open-source WCMS will be the main factors influencing their future website transformation and upgrading of academic libraries.