bluuebunny/crossref_metadata_2025 huggingface dataset:
TỶ LỆ GIẢM MẬT ĐỘ XƯƠNG Ở PHỤ NỮ MÃN KINH TỪ 45 TUỔI TRỞ LÊN TẠI TỈNH LÂM ĐỒNG NĂM 2023 VÀ MỘT SỐ YẾU TỐ LIÊN QUAN
Authors: Đặng Ngọc Hùng , Nguyễn Đỗ Huy , Trần Quốc Cường , Đinh Thị Thu Hằng
Published: November 2024

Mục tiêu: Xác định tỉ lệ giảm mật độ xương ở phụ nữ mãn kinh từ 45 tuổi trở lên tại tỉnh Lâm Đồng nằm 2023 và một số yếu tố liên quan.
Phương pháp: Nghiên cứu cắt ngang mô tả được tiến hành từ tháng 6 năm 2022 đến hết tháng 6 năm 2023 trên 681 phụ nữ mãn kinh từ 45 tuổi trở lên tại tỉnh Lâm Đồng.
Kết quả: Tỉ lệ giảm mật độ xương ở phụ nữ mãn kinh từ 45 tuổi trở lên tại tỉnh Lâm Đồng năm 2023 là 83,5%. Các yếu tố liên quan đến giảm mật độ xương ghi nhận trong nghiên cứu là độ tuổi, nghề nghiệp, số con, thời gian mãn kinh, thói quen sử dụng chè/trà, cà phê và vận động thể lực. .
Kết luận: Các nghiên cứu chuyên sâu về sự tác động của chế độ dinh dưỡng cần được tiến hành, từ đó các chương trình sàng lọc và can thiệp sớm trong cộng đồng đối với nhóm phụ nữ mãn kinh từ 45 tuổi trở lên để phát hiện sớm và dự phòng giảm mật độ xương.
Mobile App Usability, Data Privacy, Brand Loyalty, and Customer Retention in Indonesia's Online Transportation Services Industry
Authors: Bahar Anisa , Prananta Arie Wahyu , Afifudin Moch , Taufikur Rahman Rahmat
Published: November 2024

This study investigates the relationship between mobile app usability, data privacy, brand loyalty, and customer retention in Indonesia's online transportation services industry. Using partial least squares structural equation modeling (PLS-SEM) to analyze data from a sample of users, the results show that both mobile app usability and data privacy significantly affect brand loyalty, which, in turn, strongly predicts customer retention. Additionally, usability and privacy have direct effects on retention. The mediation analysis highlights the role of brand loyalty in amplifying the impact of usability and privacy on retention. These findings offer practical insights for online transportation service providers seeking to improve customer experiences, loyalty, and retention.
Robust Decision Support System for Stress Prediction Using Ensemble Techniques
Authors: Latif Dr Sohaib
Published: November 2024

The prevalence of stress among students has become a significant concern, impacting academic performance and overall well-being. Low-level stress can be beneficial as it helps us maintain focus on our daily routines, enabling us to perform tasks efficiently. However, severe stress can lead to heart disease, diabetes, aches, pains, and numerous other serious health issues. To address this issue, this research presents a comprehensive Decision Support System (DSS) tailored for predicting student stress levels using ensemble techniques. The goal of this project is to develop an ensemble model method of voting to classify the student dataset. An ensemble model is a combination of individual machine-learning algorithms that enhance the accuracy of the model by taking different approaches to the problem and their summary output through base classifiers. Extensive experimentation and validation are conducted using real-world student data to assess the effectiveness of the proposed DSS. In this research, we employed an ensemble modeling voting approach to classify the student dataset. Ensemble models integrate individual machine learning algorithms to enhance accuracy by leveraging the combined output of the base classifiers. Gradient Boosting Classifier (GBC), Random Forest (RF), Bagging Classifier (BC), and Extra Trees Classifier (ETC) served as base learners to construct a voting ensemble model for student dataset classification. Our findings revealed that the voting classifier consisting of Support Vector Machine (SVM), Random Forest (RF), Extra Trees Classifier (ETC), and Bagging Classifier (BC) yielded the highest accuracy at 94.78%, while GBC achieved 84.39% accuracy, RF attained 94.26% accuracy, ETC achieved 94.02% accuracy, and BC obtained 93.65% accuracy. The ensemble modeling approach significantly outperformed individual machine learning algorithms, demonstrating superior accuracy by mitigating variance and classification errors. Implementing such a system not only enhances the student assessment process in the education sector but also fosters interdisciplinary research collaboration across diverse fields within the region.
Deep Learning-Based Segmentation and Prediction of Liver Cancer Using CT Images
Authors: Mirza Abdul Wahab , Jaffer Arfan
Published: November 2024

Background:
Liver cancer presents a significant global health challenge, particularly in Asia, due to high incidence rates and the difficulty of early detection. Despite advances in medical imaging, the accurate segmentation and prediction of liver tumors remain critical for effective treatment planning. Deep-learning is a revolutionary tool in medical imaging, offering promising solutions for automated analysis of liver cancer using CT images.
Methods:
This study develops a framework based on deep-learning- for the segmentation, and prediction cancer of the liver from Computed Tomography images. The framework leverages advanced convolutional neural network (CNN) architectures, including U-Net and its variants, optimized with class balancing methods. The model is trained and authenticated using a high-quality annotated images, dataset of liver CT images. Key techniques such as transfer learning, data augmentation, and hyper parameter tuning are employed to enhance model performance. Evaluation metrics include accuracy, sensitivity, specificity, Dice-coefficient, and (IoU).Intersection over Union
Results:
The design of proposed program achieved significant improvements in segmentation, accuracy, with an average Dice, Similarity-Coefficient (DSC) of 0.96 for liver segmentation and 0.84 for the tumor segmentation. The results demonstrate the superior performance  of the model's compared to the other traditional methods and other existing deep learning models. Cascaded fully convolutional networks (FCNs) combined with 3D Conditional Random Fields (CRFs) showed robust performance, achieving true value accuracy rates of approximately 99.55% for liver segmentation.
Conclusion:
This study presents a novel and effective deep learning framework for liver cancer segmentation and prediction, highlighting the clinical applicability and relevance of the developed models in real-world medical settings. The findings highlight the potential in deep-learning for enhancing liver cancer diagnosis, facilitating early detection, timely interventions, and improved patient outcomes. This research contributes in increasing the body of knowledge in medical image analysis, enlighten the way for future advancements in the field.
Keywords: Computed Tomography, Deep Learning, Machine Learning, U-Net, 3D, Segmentation, Fully Convolutional Networks, Conditional Random Fields, Dice Similarity Coefficient, Intersection over Union, Conventional Neural Network
Deep Secrecy: Advancing Image Steganography Via Deep Learning Technique
Authors: Khan Mehwish , Khalid Aleena , Nasim Fawad
Published: November 2024

In today's internet-driven world, while seamless connectivity and social media make tasks easier, they also bring risks like hacking and data theft. Steganography is an emerging field that hides various data types such as images, videos, texts, and audio to protect sensitive information shared online. This term, derived from the Greek words "steganos" (hidden) and "graph" (writing/drawing), reflects the essence of this practice: hiding messages in plain sight. This technique combines science and art to ensure information concealment, addressing the growing need for secure data exchange. To deal with the data theft issues the proposed methodology presents a novel Image Steganography technique using 2 networks one is the hiding network and the other is the revealing network built on the foundation of CNN-based UNeT Architecture. Equipped with specialized encoder and decoder components, the proposed model masterfully hides secret pictures within cover images, all while preserving the cover image's natural quality. The hiding network hides the secret image within the cover image and generates the stegano image while the revealing network extracts the secret image from the cover image and reveals the original secret image. The dataset used in this research is the “COCO dataset” from where random 7500 images were used to train the model. The proposed model successfully produced a Peak Signal Noise Ratio (PSNR) value of 41.08 db. The goal is to produce undetectable stego images making its detection practically difficult.
Crop Yield Prediction Using Machine-Learning
Authors: Anwar Muhammad haziq
Published: November 2024

This study examines the utilization of machine learning techniques for forecasting agricultural yields. This is critical because it helps to improve farm productivity and food security. Agriculture is the backbone of Pakistan’s economy, and accurate crop yield prediction can greatly assist in economic planning and allocation of resources. “Several ML algorithms, including K-Nearest Neighbor, Lasso regression, Ridge Regression, Decision Tree, and Linear Regression,” were used in this research to forecast crop yield using historical data and climate variables. Among all the models developed, the KNN algorithm was the most effective, with MAE=108023825.91 and R2=0.98494, meaning that it had the highest accuracy. These results suggest that ML has a large potential to give good predictions about crop yields, which would be useful for decision-making in agriculture and crop management or strategy development. The paper also enlightens on the limitations of some algorithms, like Decision Trees, known for their low-performance abilities; it suggests other ensemble models as better alternatives to increase predictive power. Machine learning integration with big data platforms and advanced computational methods offers a promising approach toward smart agriculture, ensuring food security.
Weather Prediction Using Machine Learning Classifiers
Authors: KHALID AWAIS
Published: November 2024

This study evaluates machine learning algorithms to increase weather predicting accuracy. However, traditional NWP models are computationally time-consuming and may fail to capture the nonlinear complexities inherent in meteorological observations. This research studies three machine learning algorithms—XGBoost, Gradient Boosting, and Random Forest. For the prediction of future conditions based on detailed patterns of past weather, these methods are adopted. The XGBoost model recorded an accuracy rate of 82.25% which is higher than the Random Forest and Gradient Boosting models. All models performed well in normal weather conditions such as "sun" and "rain," but were least competent at predicting less common phenomena like "drizzle," "fog," and "snow." This demonstrates the challenge of making predictions when unbalanced data sets are used in weather forecasting exercises. The incorporation of such machine learning techniques with conventional NWP models has the potential to result in improved prediction performance in various applications.
Wine Quality Prediction Using Machine Learning
Authors: Arshad Humair
Published: November 2024

Quality prediction of wine is a significant feature of the wine business and directly affects revenue, competition, and customer happiness. However, traditional approaches—like sensory assessment and chemical analysis—have generally been considered to have limits in terms of precision and effectiveness. This research compared which Random Woods, XGBoost logarithmic regression, Choice Tree Classifier, and k-nearest-neighbor algorithms were most effective in building and evaluating artificial intelligence classifiers for predicting the taste of red and white wines based on biological and physical attributes. We selected features and building strategies to improve the model on a dataset with 1,599 wine specimens with 13 molecular and physical properties. The trained models were then evaluated with reliability, precision, recollection, F1-score, and ROC-AUC metrics. In this regard, our findings indicated that class imbalance had been handled since the Random Forest and XGBoost gave the highest accuracy rates at 0.950158 and 0.950158, respectively. Unsurprisingly, the most important features that affect wine quality are pH, pH level, and sugar concentration. This case study Exhibit demonstrates how machine learning can appropriately and effectively forecast the quality of wine, providing producers with insightful information and thereby helping to build systems that will finally pay dividends to the wine business and the customers by making precise and successful prediction systems.
Utilizing Deep Learning Techniques for Crude Oil Prices Forecasting
Authors: Ali Ahmad , Khuram Muhammad Khuram Zafar , Iftikhar Naseer
Published: November 2024

The price of crude oil has a major effect on the global economy. Over the previous few years, the price of crude oil has fluctuated more than the price of any other commodity. Since the price of crude oil is based on several outside variables and significant volatility Predicting the price of crude oil is extremely difficult. Recurrent neural network-based Long Short-Term Memory (LSTM) has demonstrated superior performance in predicting highly volatile prices. This model is used to assess and predict major crude oil prices. The useful data from the WTI unrefined petroleum markets is used to evaluate the suggested model's display. The preliminary results show that the proposed model delivers increases in the predicted level of outcome precision.The necessary information is taken from the official website of the Federal Reserve Bank of St. Louis to generate an output of 0.951826 for accuracy and 0.041717 for RMSE errors based on the 100-epoch LSTM.
Evaluating the Performance of Five Classifiers for Twitter Sentiment Analysis Using Bag of Words
Authors: Munir Qasim
Published: November 2024

Abstract: Twitter is a social medium for millions of people to express their opinions on different subjects. Natural Language Processing is widely used for sentiment analysis. This Research paper compares five machine learning classifier performances using a bag of words technique. A Kaggle Dataset is used. The Naïve Bayes classifier gave better predictions having an accuracy achieved to 91.59 percent for the four type sentiments from the text, Negative, Positive, Neutral, and Irrelevant. The classifiers used in this research are Logistic Regression, XGBoost, Naïve Bayes, Random Forest, and Support Vector Machine. The bag of words with having n-grams to four words and retaining all the stop words gave better accuracy than having n-grams to one word and removing all stop words. For further improvement in results Transformer based models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) must be taken into account. Deep learning models are more likely to give better accuracy than traditional machine learning models, and they use word embeddings rather than a bag of words technique to understand semantics better.
InceptionResNetV2 Based Eye Cataract Detection
Authors: Siddique Abu Bakar , Fakhar Ur Rehman  , AbuZar Shahid  , Sabhee Ul Hassan  , Fawad Nasim
Published: November 2024

An eye lens is a key source for visualizing things. Sometimes it may be cloudy, caused by cataracts, and disturb the visualization process. It is necessary to detect and treat the cataract in its early stages; otherwise, it may lead to blindness. For the early detection of eye cataracts, many solutions have been proposed in past years by experts. However, it requires an expert ophthalmologist or eye surgeon to detect cataracts in its early stages because early detection of cataracts is a challenging task. Now this is the era of digitalization and AI. Everyone is talking about AI. Many researchers use AI to propose a solution to this problem. This study also proposed an AI-based cataract detection solution. A binary class eye fundus images data set was used for this research work. The ”Cataract” data set is a famous dataset for detecting cataracts on KAGGLE. Some image augmentation techniques were used to enhance the data set proficiency. In this study, InceptionResNetV2 was applied with some hyper-parameter tuning. The proposed methodology achieved a training accuracy of 99% and a validation accuracy of 98%.
Real-Time Internet-based Technology Applying Machine Learning Algorithm to Identify Airplane
Authors: Muhammad Ibrar  , Saif Ur Rehman  , Zaidi Tahir , Javaid Rida , Ellahi Manzoor , Hashmi Wajih H.
Published: November 2024

Aircraft identification is critical in remote sensing for both civil and military purposes. The main challenge arises from subtle characteristics that cause significant variation within the same segment, such as differences in size, posture and angle. , while only minor differences exist. Among similar subcategories, it is important to accurately identify aircraft types such as radar systems, although commonly used, and often lacking accuracy. Anaconda, a state-of-the-art object recognition algorithm for aircraft recognition in the environment, achieves 98% training accuracy and 75% artificial intelligence accuracy by allowing computer systems to recognize objects from visual images or videos. Allows for analysis. The system is further equipped with an audio alert feature that activates when a potentially dangerous aircraft is detected. The project uses Python 3.6 (or later) and the necessary Python modules is, which makes it efficient and effective
AI-Based Network Traffic Analysis for Threat Hunting
Authors: Usman Ghani Muhammad , Ahmad Aftab , Nasim Fawad , Ahmad Jawad , Mumtaz Gohar
Published: November 2024

The focus of this study is to explore the effectiveness of advanced models like CNN and RNN to detect cyber threats on our network systems that are posted to us with every passing day. By using the CIC-DDoS 2019 dataset we test the possibility of detecting threats on network traffic in a realistic environment. This study tests both the limitations and strengths of neural networks for our case study. Our aim is to make a system efficient enough to improve itself over time and mitigate the threats posed in future by predicting them beforehand. This involves difficult testing of the CNN and RNN models to identify any patterns or anomalies that are indicate DDos attacks. This will work with the integration of AI with Cybersecurity and working on the real-world data and improve the accuracy and speed of these models to work efficiently. This will help make our environment resistant to the threats that will emerge with the advent of technology. These findings are intended to inform the development of more robust and autonomous AI systems that safeguard Internet while respecting user privacy.
Auto Insurance Churn Using Machine Learning Models
Authors: Ishtiaq Usman
Published: November 2024

This paper dives into the existing churnScience prediction methods over different segments such as Web administrations, gaming, protections, and administration. For companies pointing at maintained development and expanded client maintenance, a vigorous client churn forecast is basic. Notwithstanding a company's benefit quality, recognizing focuses where clients suspend employing a benefit, item, or platform holds noteworthy significance. Typically, it is especially pivotal for different businesses, recognizing that holding current clients is regularly more cost-effective than acquiring unused ones. The essential commitment of this inquiry lies within the development of a proactive prescient demonstration able to predict clients who are likely to terminate employing a benefit, item, or stage. They consider utilizing machine learning strategies, particularly on auto protection churn information. Another outstanding commitment is the utilization of the XGBooster algorithm, Gradient Boosting, and Random Forest Tree for data labeling, pointing to improve accuracy. The data is apportioned into 70% for training and 30% for testing, with hyperparameter tuning utilized to optimize the machine learning demonstration. So, we can compare all the algorithms for better accuracy of data and obtain an improved accuracy is 88.95% and the F1 score is 94% respectively.
Support Vector Machine Based DDoS Detection and Mitigation in Software Defined Networks
Authors: Rasheed Sadia , Rathore Muhammad Siraj
Published: November 2024

The Software Defined Networking (SDN) is an innovative network architecture that offers flexible and programable networks through a centralized controller. However, If the controller fails the whole system becomes paralyzed. The Distributed Denial of Service (DDoS) attack is one of the main threats to the SDN controller, as it exhausts the resources of the SDN controller which disturbs the whole network and affects the performance of the network. In this regard, we propose a Support Vector Machine (SVM) based machine learning classifier to detect the DDoS attacks on SDN controller. Once the attack is detected by the classifier, mitigation module is invoked to block the attack flows. The objective is to detect DDoS attacks with high accuracy and mitigate them in a short period of time. We evaluate our proposed solutions on two different publicly available datasets. i.e., KDD and KDD’99
Editors' Introduction
Authors: Jansen Rob , Shafiq Zubair
Published: November 2024

Editors' Introduction, Issue 1 of PETS Volume 2025
Understanding Privacy Norms through Web Forms
Authors: Cui Hao , Trimananda Rahmadi , Markopoulou Athina
Published: November 2024

Web forms are one of the primary ways to collect personal information online, yet they are relatively under-studied. Unlike web tracking, data collection through web forms is explicit and contextualized. Users (i) are asked to input specific personal information types, and (ii) know the specific context (i.e., on which website and for what purpose). For web forms to be trusted by users, they must meet the common sense standards of appropriate data collection practices within a particular context (i.e., privacy norms). In this paper, we extract the privacy norms embedded within web forms through a measurement study. First, we build a specialized crawler to discover web forms on websites. We run it on 11,500 popular websites, and we create a dataset of 293K web forms. Second, to process data of this scale, we develop a cost-efficient way to annotate web forms with form types and personal information types, using text classifiers trained with assistance of large language models (LLMs). Third, by analyzing the annotated dataset, we reveal common patterns of data collection practices. We find that (i) these patterns are explained by functional necessities and legal obligations, thus reflecting privacy norms, and that (ii) deviations from the observed norms often signal unnecessary data collection. In addition, we analyze the privacy policies that accompany web forms. We show that, despite their wide adoption and use, there is a disconnect between privacy policy disclosures and the observed privacy norms.
Lightweight Two-Party Secure Sampling Protocol for Differential Privacy
Authors: Kii Masanobu , Ichikawa Atsunori , Miura Takayuki
Published: November 2024

Secure sampling is a secure multiparty computation protocol that allows a receiver to sample random numbers from a specified non-uniform distribution. It is a fundamental tool for privacy-preserving analysis since adding controlled noise is the most basic and frequently used method to achieve differential privacy. The well-known approaches to constructing a two-party secure sampling protocol are transforming uniform random values into non-uniform ones by computations (e.g., logarithm or binary circuits) or table-lookup. However, they require a large computational or communication cost to achieve a strong differential privacy guarantee. This work addresses this problem with our novel lightweight two-party secure sampling protocol. Our protocol consists of random table-lookup from a small table with the 1-out of-n oblivious transfer and only additions. Furthermore, we provide algorithms for making a table to achieve differential privacy. Our method can reduce the communication cost for (1.0, 2^(-40))-differential privacy from 183GB (naive construction) to 7.4MB.
RSA Blind Signatures with Public Metadata
Authors: Amjad Ghous , Yeo Kevin , Yung Moti
Published: November 2024

Anonymous tokens are, essentially, digital signature schemes that enable issuers to provide users with signatures without learning the user inputs or the final signatures. These primitives allow applications to propagate trust while simultaneously protecting the user identity. They have become a core component for improving the privacy of several real-world applications including ad measurements, authorization protocols, spam detection, and VPNs. In certain applications, it is natural to associate signatures with specific public metadata, ensuring that trust is only propagated with respect to only a certain set of users and scenarios. To solve this, we study the notion of anonymous tokens with public metadata. We present a variant of RSA blind signatures with public metadata where issuers may only generate signatures that verify for a certain choice of public metadata that is a modification of a scheme by Abe and Fujisaki. Our protocol exclusively uses standard cryptography with widely available implementations. We prove security from the one-more RSA assumptions with multiple exponents that we introduce. Furthermore, we provide evidence that the concrete security bounds should be nearly identical to standard RSA blind signatures. We show that our protocol incurs minimal overhead over standard RSA blind signatures and report anonymous telemetry for a real-world deployment to showcase its scalability. Following our work, our protocol has been proposed as a technical specification in an IRTF internet draft.
Identifying Privacy Personas
Authors: Hrynenko Olena , Cavallaro Andrea
Published: November 2024

Privacy personas capture the differences in user segments with respect to one’s knowledge, behavioural patterns, level of self-efficacy, and perception of the importance of privacy protection. Modelling these differences is essential for appropriately choosing personalised communication about privacy (e.g. to increase literacy) and for defining suitable choices for privacy enhancing technologies (PETs). While various privacy personas have been derived in the literature, they group together people who differ from each other in terms of important attributes such as perceived or desired level of control, and motivation to use PET. To address this lack of granularity and comprehensiveness in describing personas, we propose eight personas that we derive by combining qualitative and quantitative analysis of the responses to an interactive educational questionnaire. We design an analysis pipeline that uses divisive hierarchical clustering and Boschloo’s statistical test of homogeneity of proportions to ensure that the elicited clusters differ from each other based on a statistical measure. Additionally, we propose a new measure for calculating distances between questionnaire responses, that accounts for the type of the question (closed- vs open-ended) used to derive traits. We show that the proposed privacy personas statistically differ from each other. We statistically validate the proposed personas and also compare them with personas in the literature, showing that they provide a more granular and comprehensive understanding of user segments, which will allow to better assist users with their privacy needs.
Beyond the Request: Harnessing HTTP Response Headers for Cross-Browser Web Tracker Detection in an Imbalanced Setting
Authors: Rieder Wolf , Raschke Philip , Cory Thomas
Published: November 2024

The World Wide Web's connectivity is greatly attributed to the HTTP protocol, with HTTP messages offering informative header fields that appeal to disciplines like web security and privacy, especially concerning web tracking. Despite existing research employing HTTP request messages to identify web trackers, HTTP response headers are often overlooked. This study endeavors to design effective machine learning classifiers for web tracker detection using binarized HTTP response headers. Data from the Chrome, Firefox, and Brave browsers, obtained through the traffic monitoring browser extension T.EX, serves as our dataset. Ten supervised models were trained on Chrome data and tested across all browsers, including a Chrome dataset from a year later. The results demonstrated high accuracy, F1-score, precision, recall, and minimal log-loss error for Chrome and Firefox, but subpar performance on Brave, potentially due to its distinct data distribution and feature set. The research suggests that these classifiers are viable for web tracker detection. However, real-world application testing remains pending, and the distinction between tracker types and broader label sources could be explored in future studies.
SoK: Descriptive Statistics Under Local Differential Privacy
Authors: Raab René , Berrang Pascal , Gerhart Paul , Schröder Dominique
Published: November 2024

Local Differential Privacy (LDP) provides a formal guarantee of privacy that enables the collection and analysis of sensitive data without revealing any individual's data. While LDP methods have been extensively studied, there is a lack of a systematic and empirical comparison of LDP methods for descriptive statistics. In this paper, we first provide a systematization of LDP methods for descriptive statistics, comparing their properties and requirements. We demonstrate that several mean estimation methods based on sampling from a Bernoulli distribution are equivalent in the one-dimensional case and introduce methods for variance estimation. We then empirically compare methods for mean, variance, and frequency estimation. Finally, we provide recommendations for the use of LDP methods for descriptive statistics and discuss their limitations and open questions.
Revealing Hidden IoT Devices through Passive Detection, Fingerprinting, and Localization
Authors: Sun Wei , Givehchian Hadi , Bharadia Dinesh
Published: November 2024

Internet-of-things (IoT) devices (e.g., micro camera and microphone) are usually small form factor, low-cost, and low-power, which makes them easy to conceal and deploy in the indoor environment to spy on people for human private information such as location and indoor activities. As a result, these IoT devices introduce a great privacy and ethical threat. Therefore, it is important to reveal these concealed IoT devices in the indoor environment for human privacy protection. This paper presents RFScan, a system that can passively detect, fingerprint, and localize diverse concealed IoT devices in the indoor environment by sensing their unintentional electromagnetic emanations. However, sensing these emanations is challenging due to the weak emanation strength and the interference from the ambient wireless communication signals. To this end, we boost the emanation strength through the non-coherent averaging based on the emanation signal's characteristics and design a novel suppression algorithm to mitigate interference from the wireless communication signals. We further profile emanations across frequency and time that act as the emanation source's unique signature and customize a deep neural network architecture to fingerprint the emanation sources. Furthermore, we can localize the emanation source with an angle-of-arrival (AoA) based triangulation approach. Our experimental results demonstrate the efficiency of the IoT devices' detection, fingerprinting, and localization across different indoor environments.
DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech
Authors: Woszczyk Dominika , Demetriou Soteris
Published: November 2024

Dementia is a sensitive neurocognitive disorder affecting tens of millions of people worldwide and its cases are expected to triple by 2050. Alarmingly, recent advancements in dementia classification make it possible for adversaries to violate affected individuals’ privacy and infer their sensitive condition from speech transcriptions. Existing obfuscation methods in text have never been applied for dementia and depend on the availability of large labeled datasets which are challenging to collect for sensitive medical attributes. In this work, we bridge this research gap and tackle the above issues by leveraging Large-Language-Models (LLMs) with diverse prompt designs (zero-shot, few-shot, and knowledge-based) to obfuscate dementia in speech transcripts. Our evaluation shows that LLMs are more effective dementia obfuscators compared to competing methods. However, they have billions of parameters which renders them hard to train, store and share, and they are also fragile suffering from hallucination, refusal and contradiction effects among others. To further mitigate these, we propose a novel method, DiDOTS. DiDOTS distills knowledge from LLMs using a teacher–student paradigm and parameter-efficient fine-tuning. DiDOTS has one order of magnitude fewer parameters compared to its teacher LLM and can be fine-tuned using three orders of magnitude less parameters compared to full fine-tuning. Our evaluation shows that compared to prior work DiDOTS retains the performance of LLMs achieving 1.3x and 2.2x improvement in privacy performance on two datasets, while humans rate it as better in preserving utility even when compared to state-of-the-art paraphrasing models.
Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression
Authors: Sarathy Jayshree , Vadhan Salil
Published: November 2024

In this paper, we study differentially private point and confidence interval estimators for simple linear regression. Motivated by recent work that highlights the strong empirical performance of an algorithm based on robust statistics, DPTheilSen, we provide a rigorous, finite-sample analysis of its privacy and accuracy properties, offer guidance on setting hyperparameters, and show how to produce differentially private confidence intervals to accompany its point estimates.
Examining Caregiving Roles to Differentiate the Effects of Using a Mobile App for Community Oversight for Privacy and Security
Authors: Akter Mamtaj , Kropczynski Jess , Lipford Heather , Wisniewski Pamela
Published: November 2024

We conducted a 4-week field study with 101 smartphone users who self-organized into 22 small groups of family, friends, and neighbors to use "CO-oPS," a mobile app for co-managing mobile privacy and security. We differentiated between those who provided oversight (i.e., caregivers) and those who did not (i.e., caregivees) to examine differential effects on their experiences and behaviors while using CO-oPS. Caregivers reported higher power use, community trust, belonging, collective efficacy, and self-efficacy than caregivees. Both groups' self-efficacy and collective efficacy for mobile privacy and security increased after using CO-oPS. However, this increase was significantly stronger for caregivees. Our research demonstrates how community-based approaches can benefit people who need additional help managing their digital privacy and security. We provide recommendations to support community-based oversight for managing privacy and security within communities of different roles and skills.
Mastic: Private Weighted Heavy-Hitters and Attribute-Based Metrics
Authors: Mouris Dimitris , Patton Christopher , Davis Hannah , Sarkar Pratik , Tsoutsos Nektarios Georgios
Published: November 2024

Insight into user experience and behavior is critical to the success of large software systems and web services. Gaining such insights, while preserving user privacy, is a significant challenge. Recent advancements in multi-party computation have made it practical to securely compute aggregates over secret shared data. Two such protocols have emerged as candidates for standardization at IETF: Prio (NSDI 2017) for general-purpose statistics; and Poplar (IEEE S&P 2021) for heavy hitters, where the goal is to compute the most popular inputs held by users without learning the inputs themselves. While each of these protocols is well-suited to certain applications, there remain a number of use cases identified by IETF for which neither Prio nor Poplar is practical.
We introduce Mastic, a protocol for the following functionality: each of a large number of clients holds an input (e.g., a URL) and its corresponding weight (e.g., page load time); for a given candidate input (or prefix), a small number of non-colluding servers wish to securely aggregate the weights of clients that hold that input (or some input with that prefix), without learning the weights or which client holds which input. This functionality makes two new classes of applications possible. The first is a natural generalization of heavy hitters we call weighted heavy-hitters. The second is an enhancement of Prio-style metrics we call attribute-based metrics in which aggregates are grouped by hierarchical user attributes (e.g., their geographic location or software version). We demonstrate Mastic's practicality for these applications with a real-world example of each. We also compare our protocol with Prio and Poplar on a wide area network. Overall, we report over one order of magnitude performance improvement over Poplar for plain heavy-hitters and 1.5-2x improvement over Prio for attribute-based metrics.
Real-World Deniability in Messaging
Authors: Collins Daniel , Colombo Simone , Huguenin-Dumittan Loïs
Published: November 2024

This work explores real-world deniability in messaging. We propose a formal model that considers the entire messaging system to analyze deniability in practice. Applying this model to the Signal application and DKIM-protected email, we demonstrate that these systems do not offer practical deniability guarantees. Additionally, we analyze 140 court cases in Switzerland that use conversations on messaging applications as evidence and find that none consider deniability, providing evidence that this property does not have an impact in the legal setting. Based on these technical and legal findings, we assess whether deniability is a desirable property and the challenges and shortcomings of designing a system that is deniable in practice. We posit that systems should either offer real-world deniability or refrain from claiming to achieve it. We discuss how to choose an appropriate threat model for deniability in a given context and how to design communication systems that are deniable in practice. For Signal, we propose and discuss a simple yet effective solution: the application should enable direct modification of locally stored messages in the user interface. This position paper raises several unanswered questions, aiming to further stimulate discussion and research on real-world deniability in messaging.
Practical Two-party Computational Differential Privacy with Active Security
Authors: Meisingseth Fredrik , Rechberger Christian , Schmid Fabian
Published: November 2024

In this work we revisit the problem of using general-purpose MPC schemes to emulate the trusted dataholder in differential privacy (DP), to achieve the same accuracy but without the need to trust one single dataholder. In particular, we consider the two-party model where two computational parties (or dataholders), each with their own dataset, wish to compute a canonical DP mechanism on their combined data and to do so with active security. We start by remarking that available definitions of computational DP (CDP) for protocols are somewhat ill-suited for such a use-case, due to them either poorly capturing some strong security guarantees commonly given by general-purpose MPC protocols, or having too strict requirements in the sense that they need significant adjustment in order to be satisfiable by using common DP and MPC techniques. With this in mind, we propose a new version of simulation-based CDP, called SIM*-CDP, and prove it to be stronger than the IND-CDP and SIM-CDP and incomparable to SIM+-CDP. We demonstrate the usability of the SIM*-CDP definition by showing how to satisfy it by the use of an available distributed protocol for sampling truncated geometric noise. Further, we use the protocol to compute two-party inner-products with CDP and active security, and with accuracy equal to that of the central model, being the first to do so. Finally, we provide an open-sourced implementation and benchmark its practical performance. Our implementation generates a truncated geometric sample in between about 0.035 and 3.5 seconds (amortized), depending on network and parameter settings, comparing favourably to existing implementations.
EpiOracle: Privacy-Preserving Cross-Facility Early Warning for Unknown Epidemics
Authors: Li Shiyu , Zhang Yuan , Song Yaqing , Wu Fan , Lyu Feng , Yang Kan , Tang Qiang
Published: November 2024

Syndrome-based early epidemic warning plays a vital role in preventing and controlling unknown epidemic outbreaks. It monitors the frequency of each syndrome, issues a warning if some frequency is aberrant, identifies potential epidemic outbreaks, and alerts governments as early as possible. Existing systems adopt a cloud-assisted paradigm to achieve cross-facility statistics on the syndrome frequencies. However, in these systems, all symptom data would be directly leaked to the cloud, which causes critical security and privacy issues. In this paper, we first analyze syndrome-based early epidemic warning systems and formalize two security notions, i.e., symptom confidentiality and frequency confidentiality, according to the inherent security requirements. We propose extsf{EpiOracle}, a cross-facility early warning scheme for unknown epidemics. EpiOracle ensures that the contents and frequencies of syndromes will not be leaked to any unrelated parties; moreover, our construction uses only a symmetric-key encryption algorithm and cryptographic hash functions (e.g., [CBC]AES and SHA-3), making it highly efficient. We formally prove the security of EpiOracle in the random oracle model. We also implement an EpiOracle prototype and evaluate its performance using a set of real-world symptom lists. The evaluation results demonstrate its practical efficiency.
BehaVR: User Identification Based on VR Sensor Data
Authors: Jarin Ismat , Duan Yu , Trimananda Rahmadi , Cui Hao , Elmalaki Salma , Markopoulou Athina
Published: November 2024

Virtual reality (VR) platforms enable a wide range of applications, however, pose unique privacy risks. In particular, VR devices are equipped with a rich set of sensors that collect personal and sensitive information (e.g., body motion, eye gaze, hand joints, and facial expression). The data from these newly available sensors can be used to uniquely identify a user, even in the absence of explicit identifiers. In this paper, we seek to understand the extent to which a user can be identified based solely on VR sensor data, within and across real-world apps from diverse genres. We consider adversaries with capabilities that range from observing APIs available within a single app (app adversary) to observing all or selected sensor measurements across multiple apps on the VR device (device adversary). To that end, we introduce BehaVR, a framework for collecting and analyzing data from all sensor groups collected by multiple apps running on a VR device. We use BehaVR to collect data from real users that interact with 20 popular real-world apps. We use that data to build machine learning models for user identification within and across apps, with features extracted from available sensor data. We show that these models can identify users with an accuracy of up to 100%, and we reveal the most important features and sensor groups, depending on the functionality of the app and the adversary. To the best of our knowledge, BehaVR is the first to analyze user identification in VR comprehensively, i.e., considering all sensor measurements available on consumer VR devices, collected by multiple real-world, as opposed to custom-made, apps.
Misalignments and Demographic Differences in Expected and Actual Privacy Settings on Facebook
Authors: Lowens Byron , Scarnecchia Sean , Im Jane , Afnan Tanisha , Chen Annie , Zou Yixin , Schaub Florian
Published: November 2024

Social media platforms pose privacy risks when data is used in unexpected ways (e.g., for advertising or data sharing with partners). Using a custom browser extension and an online survey with 195 Facebook users, we investigated (1) whether participants’ expected values of their Facebook privacy settings were (mis)aligned with their actual settings; (2) demographic differences in privacy expectation-setting mismatches; and (3) participants' privacy concerns and trust towards Facebook.Our study presents a current and comprehensive analysis of Facebook users' privacy settings. We find that expectation-setting mismatches are prevalent: all participants had at least one mismatch; many had multiple, often expecting their settings to be more restrictive than they were. We also found that Facebook's default values are not aligned with people's expectations and/or actual settings, which suggests that those defaults are ineffective. Furthermore, mismatches differed along certain demographic variables.Participants' trust in Facebook decreased after they became aware of mismatches and their actual settings. Our empirical findings indicate that, despite increased public awareness, media scrutiny, and regulatory attention regarding privacy issues, there is still a substantial and concerning disconnect between how private people perceive their social media data to be and how exposed their data actually is, opening them up to both interpersonal and institutional privacy risks. We discuss design and public policy implications of our findings.
SoK: (Un)usable Privacy: the Lack of Overlap between Privacy-Aware Sensing and Usable Privacy Research
Authors: Iravantchi Yasha , Emami-Naeini Pardis , Sample Alanson
Published: November 2024

As the number of smart devices increases in our lives, the data they collect to perform valuable tasks, such as voice assistant requests, comes at the cost of user privacy. To mitigate their privacy impact, emerging usable privacy-aware sensing (UPAS) research has relied on cross-disciplinary approaches that extend past the core focus of broader academic research communities, such as Security & Privacy or Human-Computer Interaction. These works incorporate privacy design principles, whereby systems include safeguards by combining usable privacy (UP) with privacy-aware sensing (PAS) design to protect users' privacy. To better understand this emerging area of research, we conducted a mixed qualitative and quantitative Systematization of Knowledge (SoK). With a thorough review of pertinent literature, resulting in 114 selected works (reduced from 10,122 across 12 venues), we found that, despite the similarity of these works, many are dispersed across multiple communities, utilize community-specific jargon and keywords, and minimally overlap in design and evaluation approaches, potentially hindering cross-pollination across communities and thereby slowing the growth of this emerging research area. Thus, these factors helped reveal a research gap in this space. We use these findings to present four research themes and provide community and design recommendations to encourage cross-disciplinary UPAS research.