OpenRaiser/Intern-Atlas huggingface dataset:
Online Sampling of Summaries from Public SPARQL Endpoints
Authors: Thi Hoang Thi Pham, Hala Skaf-Molli, Pascal Molli, Brice Nédelec
Venue: WWW
Year: 2024

Collecting statistics from online public SPARQL endpoints is hampered by their fair usage policies. These restrictions hinder several critical operations, such as aggregate query processing, portal development, and data summarization. Online sampling enables the collection of statistics while respecting fair usage policies. However, sampling has not yet been integrated into the SPARQL standard. Although integrating sampling into the SPARQL standard appears beneficial, its effectiveness must be demonstrated in a practical semantic web context. This paper investigates whether online sampling can generate summaries useful in cutting-edge SPARQL federation engines. Our experimental studies indicate that sampling allows the creation and maintenance of summaries by exploring less than 20% of datasets.
GRASP: Hardening Serverless Applications through Graph Reachability Analysis of Security Policies
Authors: Isaac Polinsky, Pubali Datta, Adam Bates, William Enck
Venue: WWW
Year: 2024

Serverless computing is supplanting past versions of cloud computing as the easiest way to rapidly prototype and deploy applications. However, the reentrant and ephemeral nature of serverless functions only exacerbates the challenge of correctly specifying security policies. Unfortunately, with role-based access control solutions like Amazon Identity and Access Management (IAM) already suffering from pervasive misconfiguration problems, the likelihood of policy failures in serverless applications is high. In this work, we introduce GRASP, a graph-based analysis framework for modeling serverless access control policies as queryable reachability graphs. GRASP generates reusable models that represent the principals of a serverless application and the interactions between those principals. We implement GRASP for Amazon IAM in Prolog, then deploy it on a corpus of 731 open source Amazon Lambda applications. We find that serverless policies tend to be short and highly permissive, e.g., 92% of surveyed policies are comprised of just 10 statements and 30% exhibit full reachability between all application functions and resources. We then use GRASP to identify potential attack vectors permitted by these policies, including hundreds of sensitive access channels, a dozen publicly-exposed resources, and four channels that may permit an attacker to exfiltrate an application's private resources through one of its public resources. These findings demonstrate GRASP's utility as a means of identifying opportunities for hardening application policies and highlighting potential exfiltration channels.
Understanding, Leveraging, and Improving Large Language Models
Authors: Soujanya Poria
Venue: WWW
Year: 2024

The emergence of Large Language Models (LLMs) has marked a substantial advancement in Natural Language Processing (NLP), contributing significantly to enhanced task performance both within and outside specific domains. However, amidst these achievements, three key questions remain unanswered: 1) The mechanism through which LLMs accomplish their tasks and their limitations, 2) Effectively harnessing the power of LLMs across diverse domains, and 3) Strategies for enhancing the performance of LLMs. This talk aims to delve into our research group's endeavors to address these pivotal questions. Firstly, I will outline our approach, which involves utilizing ontology-guided prompt perturbations to unravel the primary limitations of LLMs in solving mathematical problems. Moving on to the second question, we will explore the utilization of synthetic data generated by LLMs to bolster challenging downstream tasks, particularly focusing on structured prediction where LLMs face persistent challenges. I will elaborate on our initiatives aimed at improving LLMs by incorporating highly effective retrieval strategies, specifically addressing the prevalent challenge of hallucinations that often plagues contemporary LLMs. Finally, I will present a technique on LLM realignment to restore safety lost during fine-tuning.
LinkGuard: Link Locally Privacy-Preserving Graph Neural Networks with Integrated Denoising and Private Learning
Authors: Yuxin Qi, Xi Lin, Ziyao Liu, Gaolei Li, Jingyu Wang, Jianhua Li
Venue: WWW
Year: 2024

Recent studies have introduced privacy-preserving graph neural networks to safeguard the privacy of sensitive link information in graphs. However, existing link protection mechanisms in GNNs, particularly over decentralized nodes, struggle to strike an optimal balance between privacy and utility. We argue that a pivotal issue is the separation of noisy topology denoising and GNN private learning into distinct phases at the server side, leading to an under-denoising problem in the noisy topology. To address this, we propose a dynamic, adaptive Link LDP framework that performs noisy topology denoising on the server side in a dynamic manner. This approach aims to mitigate the impact of local noise on the GNN training process, reducing the uncertainty introduced by local noise. Furthermore, we integrate the noise generation and private training processes across all existing Link LDP GNNs into a unified framework. Experimental results demonstrate that our method surpasses existing approaches, obtaining around a 7% performance improvement under strong privacy strength and achieving a better trade-off between utility and privacy.
Critical Nodes Detection: Node Merging Approach
Authors: Hongbo Qiu, Renjie Sun, Chen Chen, Xiaoyang Wang, Ying Zhang
Venue: WWW
Year: 2024

Various cohesive models are widely employed for the analysis of social networks to identify critical users or key relationships, with the k-core being a particularly popular approach. Existing works, such as the anchor k-core problem, aim to maximize k-core by anchoring nodes (the degree of anchor nodes are set as infinity). However, we find that node merging can also enlarge the k-core size. Different from anchoring nodes, nodes merging can cause both degree increase and decrease which brings more challenges. In this paper, we study the core maximization by node merging problem (CMNM) and prove its hardness. A greedy framework is first presented due to its hardness. To scale for large networks, we categorize potentially influential nodes and provide a detailed analysis of all node merging pairs. Then, based on these analyses, a fast and effective algorithm is developed. Finally, we conduct comprehensive experiments on real-world networks to evaluate the effectiveness and efficiency of the proposed method.
Leveraging Knowledge-aware Methodologies for Multi-document Summarization
Authors: Yutong Qu
Venue: WWW
Year: 2024

With the development of information technology, a large amount of information and corpora has been incrementally sparked from the Web, stimulating an increasingly high demand for summarizing. Document Summarization is one of Natural Language Processing tasks, which aims to generate abridged versions of a given single or multiple documents as concise and coherent as possible while preserving salient information from the source texts. Recent research in the area has started to use knowledge graphs as they can capture more factual and applicable information from more facets along with source information, benefiting fact consistency and informativeness of generated summaries, rather than just from a linguistic perspective. However, there is no explicit investigation of the effects of different kinds of knowledge graphs on document summarization. The proposed method is to use structured informative and knowledgeable auxiliary information, especially knowledge graphs, into pre-trained summarization models, advancing summary qualities. Expected outcomes are exploring knowledge and knowledge graph incorporation for multi-document summarization, and achieving more informative, coherent, and factually consistent summaries.
Distributed Transparent Data Layer for Next Generation Blockchains
Authors: Li Quan
Venue: WWW
Year: 2024

Distributed Transparent Data Layer (DTDL) aims to overcome the significant storage inefficiencies in blockchain technology. The proposed scheme enhances scalability and enables broader adoption by allowing nodes to store only portions of the blockchain history, diverging from traditional methods like sharding. It consists of three main components: a transparent authentication scheme, a verifiable search tree, and a data availability sampling scheme, supporting diverse applications including zero-knowledge machine learning. This approach not only maintains transparency to the blockchain's upper layer but also offers seamless integration with existing systems without requiring forks. Additionally, the paper introduces an innovative transparent authentication method for Luby Transform (LT) codes using KZG commitments, enabling efficient and secure verification of encoded symbols without decoding. Addressing the challenges of data outsourcing in blockchain, our proposed model ensures data integrity and robust security in a potentially malicious publisher environment, marking a significant advancement in blockchain storage and data integrity solutions.
Unfiltered: Measuring Cloud-based Email Filtering Bypasses
Authors: Sumanth Rao, Enze Liu, Grant Ho, Geoffrey M. Voelker, Stefan Savage
Venue: WWW
Year: 2024

Email service has increasingly been outsourced to cloud-based providers and so too has the task of filtering such messages for potential threats. Thus, customers will commonly direct that their incoming email is first sent to a third-party email filtering service (e.g., Proofpoint or Barracuda) and only the "clean" messages are then sent on to their email hosting provider (e.g., Gmail or Microsoft Exchange Online). However, this loosely coupled approach can, in theory, be bypassed if the email hosting provider is not configured to only accept messages that arrive from the email filtering service. In this paper we demonstrate that such bypasses are commonly possible. We document a multi-step methodology to infer if an organization has correctly configured its email hosting provider to guard against such scenarios. Then, using an empirical measurement of edu and com domains as a case study, we show that 80% of such organizations making use of popular cloud-based email filtering services can be bypassed in this manner. We also discuss reasons that lead to such misconfigurations and outline challenges in hardening the binding between email filtering and hosting providers.
Using Model Calibration to Evaluate Link Prediction in Knowledge Graphs
Authors: Aishwarya Rao, Narayanan Asuri Krishnan, Carlos R. Rivero
Venue: WWW
Year: 2024

Link prediction models assign scores to predict new, plausible edges to complete knowledge graphs. In link prediction evaluation, the score of an existing edge (positive) is ranked w.r.t. the scores of its synthetically corrupted counterparts (negatives). An accurate model ranks positives higher than negatives, assuming ascending order. Since the number of negatives are typically large for a single positive, link prediction evaluation is computationally expensive. As far as we know, only one approach has proposed to replace rank aggregations by a distance between sample positives and negatives. Unfortunately, the distance does not consider individual ranks, so edges in isolation cannot be assessed. In this paper, we propose an alternative protocol based on posterior probabilities of positives rather than ranks. A calibration function assigns posterior probabilities to edges that measure their plausibility. We propose to assess our alternative protocol in various ways, including whether expected semantics are captured when using different strategies to synthetically generate negatives. Our experiments show that posterior probabilities and ranks are highly correlated. Also, the time reduction of our alternative protocol is quite significant: more than 77% compared to rank-based evaluation. We conclude that link prediction evaluation based on posterior probabilities is viable and significantly reduces computational costs.
Unraveling the Tangle of Disinformation: A Multimodal Approach for Fake News Identification on Social Media
Authors: Junaid Rashid, Jungeun Kim, Anum Masood
Venue: WWW
Year: 2024

The growth of interactive and multimedia content on the Internet has made it an essential news source for people worldwide. Social media is a platform for sharing information and facilitates the spread of fake news. The dissemination of disinformation on social media has a significant impact on society. Conventional methods used in the identification of fake news often struggle to analyze textual, visual, and combined aspects of news shared on social media. Therefore, we propose the Multimodal Approach for Fake News Identification (MuAFaNI), which uses a combined representation of text and images to assess news authenticity as fake or real. MuAFaNI uses the RoBERTa language model for text analysis and ResNet-50 for image analysis. Experiments on two prominent social media datasets, Twitter and Weibo, showed that MuAFaNI performed better than state-of-the-art fake news techniques in terms of accuracy, precision, recall and F1 score.
One-shot Pairing and Authentication Using Moms Secret
Authors: Ubaid Ur Rehman, Sungyoung Lee
Venue: WWW
Year: 2024

The existing pairing and authentication mechanisms adopt either fuzzy commitment or fuzzy password-authenticated key exchange for device fingerprint generation, detecting and correcting multiple symbol errors, leading to guessing attacks and increased pairing time. In this study, we propose a one-shot pairing and authentication approach that generates a device fingerprint from the selected contextual data using Median-of-medians (Moms), ensuring randomness and preventing guessing attacks. Moreover, we integrate the Moms secret into Password Authenticated Key Exchange (PAKE) to reduce the pairing time and improve security. The evaluation demonstrates that our proposed one-shot pairing and authentication approach ensures strong resistance against information gain, reduces the probability of guessing attacks, and significantly decreases the pairing time compared to state-of-the-art approaches.
Unsupervised Search Algorithm Configuration using Query Performance Prediction
Authors: Haggai Roitman
Venue: WWW
Year: 2024

Search engine configuration can be quite difficult for inexpert developers. Instead, an auto-configuration approach can be used to speed up development time. Yet, such an automatic process usually requires relevance labels to train a supervised model. In this work, we suggest a simple, yet highly effective, extension to the probabilistic query performance prediction (QPP) framework that allows to auto-configure search algorithms without relevance labels. Our solution only assumes the availability of a sample of queries in a given domain. We demonstrate the merits of our solution using two common auto-configuration tasks. The first task is similarity model selection, including selection over "compound'' models such as re-ranking and fusion. The second one is similarity parameter auto-tuning, where we choose amongst dozens of possible parameter configurations so to optimize the potential search quality.
Personalized Ordering of Recommendation-Modules on an E-Commerce Homepage
Authors: Haggai Roitman, Alexander Nus, Yotam Eshel
Venue: WWW
Year: 2024

The homepage of an E-Commerce website may accommodate multiple and diverse recommendation modules; with each module is designed to cover some facet of the user's needs. Commonly, the recommendation modules are ordered in the same way for all homepage users, which leads to a sub-optimal user experience. In this work, we present a novel personalized module ordering solution that provides a more educated way to determine an ordering of the homepage modules based on historical user-interactions. Overall, we evaluate our solution and demonstrate its merits.
DISKCO : Disentangling Knowledge from Cross-Encoder to Bi-Encoder
Authors: Ankith M. S, Arindam Bhattacharya, Ankit Gandhi, Vijay Huddar, Atul Saroop, Rahul Bhagat
Venue: WWW
Year: 2024

In the field of Natural Language Processing (NLP), sentence pair classification is important in various real-world applications. Bi-encoders are commonly used to address these problems due to their low-latency requirements, and their ability to act as effective retrievers. However, bi-encoders often under-perform compared to cross-encoders by a significant margin. To address this gap, many Knowledge Distillation (KD) techniques have been proposed. Most existing KD methods focus solely on utilizing the prediction scores of cross-encoder models and overlook the fact that cross-encoders and bi-encoders have fundamentally different input structures. In this work, we introduce a novel knowledge distillation approach called DISKCO, which DISentangles the Knowledge learned in Cross-encoder models especially from multi-head cross-attention models and transfers it to bi-encoder models. DISKCO leverages the information encoded in the cross-attention weights of the trained cross-encoder model, and provide it as contextual cues for the student bi-encoder model during training and inference. DISKCO combines the benefits of independent encoding for low-latency applications with the knowledge acquired from cross-encoders, resulting in improved performance. Empirically, we demonstrate the effectiveness of DISKCO on proprietary and on various publicly available datasets. Our experiments show that DISKCO outperforms traditional knowledge distillation methods by upto 2%.
LLM Driven Web Profile Extraction for Identical Names
Authors: Prateek Sancheti, Kamalakar Karlapalem, Kavita Vemuri
Venue: WWW
Year: 2024

The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT's contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT -- a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual.
REAL-UP: Urban Perceptions From LBSNs Helping Moving Real-Estate Market to the Next Level
Authors: Frances Albert Santos, Thiago H. Silva, Leandro A. Villas
Venue: WWW
Year: 2024

Finding the best short- or long-term accommodation is troublesome in unknown areas. Current tools provided by the real-estate market offer valuable information regarding the property, such as price, photos, and descriptions of the space; however, this market has little explored other relevant information regarding the surrounding area, such as what is nearby and users' subjective perception of the property's area. To address this gap, we propose REAL-UP, an interactive tool designed to enrich real-estate marketplaces. In addition to information commonly provided by such applications, e.g., rent price, REAL-UP also provides subjective neighborhood information based on Location-Based Social Networks (LBSNs) messages. This novel tool helps to represent complex users' subjective perceptions of urban areas, which could ease the process of finding the best accommodation.
Hyperlink Hijacking: Exploiting Erroneous URL Links to Phantom Domains
Authors: Kevin Saric, Felix Savins, Gowri Sankar Ramachandran, Raja Jurdak, Surya Nepal
Venue: WWW
Year: 2024

Web users often follow hyperlinks hastily, expecting them to be correctly programmed. However, it is possible those links contain typos or other mistakes. By discovering active but erroneous hyperlinks, a malicious actor can spoof a website or service, impersonating the expected content and phishing private information. In 'typosquatting,' misspellings of common domains are registered to exploit errors when users mistype a web address. Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these 'hijackable hyperlinks' exist in large quantities with the potential to generate substantial traffic. Analyzing large-scale crawls of the web using high-performance computing, we show the web currently contains active links to more than 572,000 dot-com domains that have never been registered, what we term 'phantom domains.' Registering 51 of these, we see 88% of phantom domains exceeding the traffic of a control domain, with up to 10 times more visits. Our analysis shows that these links exist due to 17 common publisher error modes, with the phantom domains they point to free for anyone to purchase and exploit for under 20, representing a low barrier to entry for potential attackers.
Detecting Generated Native Ads in Conversational Search
Authors: Sebastian Schmidt, Ines Zelch, Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast
Venue: WWW
Year: 2024

Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate responses to queries. It is only a small step to also let the same technology insert ads within the generated responses - instead of separately placing ads next to a response. Inserted ads would be reminiscent of native advertising and product placement, both of which are very effective forms of subtle and manipulative advertising. Considering the high computational costs associated with LLMs, for which providers need to develop sustainable business models, users of conversational search engines may very well be confronted with generated native ads in the near future. In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i.e., to block generated native ads. We compile the Webis Generated Native Ads 2024 dataset of queries and generated responses with automatically inserted ads, and evaluate whether LLMs or fine-tuned sentence transformers can detect the ads. In our experiments, the investigated LLMs struggle with the task but sentence transformers achieve precision and recall values above 0.9.