The study investigates text input field security in web browsers, revealing that browsers’ permission model breaches security principles. Two vulnerabilities are found, including passwords exposed as plaintext in HTML source code. A proof-of-concept extension demonstrates the impact, bypassing review processes. The vulnerabilities are widespread, affecting major sites like Google. Around 12.5% of extensions could exploit these flaws, with 190 extensions accessing passwords directly. The study proposes remedies: a JavaScript package for developers to safeguard input fields and a browser-level alert for sensitive field access. The research underscores the demand for enhanced online user information protection.

Google now requires developers to implement Data Safety Sections (DSS) for improved transparency in data collection and sharing. A research paper analyzes Google’s DSS through quantitative and qualitative methods, examining a large number of apps (1.1 million) from the Android Play store. The study identifies inconsistencies and instances of both over and under-reporting practices in DSS content. A longitudinal analysis indicates that developers are still adapting their practices over time. The research also delves into the challenges developers face when working with DSS, highlighting the need for better resources and guidelines to ensure accurate and reliable privacy labels, which are crucial for their effectiveness.

Privacy nutrition labels are used to understand app data practices. These labels are mandated by Apple and Google on their app platforms. By analyzing the labels, it is possible to understand app data practices at scale. Popular apps tend to have better privacy practices than less popular apps. There is often a discrepancy between an app’s privacy policy and its label.

Hark is a system for discovering and summarizing privacy-related feedback at scale. The system uses deep learning models to automate the process of summarizing feedback, starting with unstructured text and resulting in a hierarchy of high-level privacy themes and fine-grained issues within each theme. Hark was tested on a corpus of 626 million Google Play reviews and was able to extract 6 million privacy-related reviews with an AUC-ROC of 0.92. The system’s generated issues were found to be of high accuracy and coverage, and the theme titles were of high quality. Hark provides high-level insights from 1.3 million Android apps.

Online services utilize privacy settings to provide users with control over their data. However, these privacy settings are often hard to locate, causing the user to rely on provider-chosen default values. In this work, we train privacy settings centric encoders and leverage them to create an interface that allows users to search for privacy settings using free-form queries.

PriSEC is a privacy settings enforcement controller that uses machine learning to automatically enforce web privacy controls. PriSEC goes beyond finding the HTML elements with privacy settings to discover fine-grained options, present them in a centralized interface, and enforce them on demand with minimal user intervention. The system uses novel algorithms to overcome the open nature of web development. In evaluations, PriSEC precisely annotated the privacy controls for 94.3% of the control pages in the evaluation set. A user study showed an average reduction of 3.75x in the time taken to adjust privacy settings compared to the baseline system.

Online services utilize privacy settings to provide users with control over their data. However, these privacy settings are often hard to locate, causing the user to rely on provider-chosen default values. In this work, we train privacy settings centric encoders and leverage them to create an interface that allows users to search for privacy settings using free-form queries. To achieve this, we create a custom Semantic Similarity dataset, which consists of real user queries covering various privacy settings. We then use this dataset to fine-tune the state of the art encoders. Using these fine-tuned encoders, we perform semantic matching between the user queries and the privacy settings to retrieve the most relevant setting. Finally, we also use these encoders to generate embeddings of privacy settings from the top 100 websites and perform unsupervised clustering to learn about the online privacy settings types.

We study the impact of the GDPR on the landscape of privacy policies online. We create a diverse corpus of two sets of 6,278 unique English-language privacy policies from inside and outside the EU, covering their pre-GDPR and the post-GDPR versions. We find evidence for positive changes triggered by the GDPR, with the specificity level improving on average. Still, we find the landscape of privacy policies to be in a transitional phase; many policies still do not meet several key GDPR requirements or their improved coverage comes with reduced specificity.

n this paper, the authors study the effects of active-neutrino-sterile-neutrino mixing on the expected high-energy astrophysical neutrino flavor content. They introduce the concept of the four-flavor tetrahedron to visualize the lack of unitarity in the astrophysical neutrino three-flavor triangle. The active-sterile neutrino mixings modify the allowed region of the astrophysical flavor ratio from the standard case, but a projection of the four-flavor tetrahedron has restrictions of phase space similar to the three-flavor triangle. The presence of astrophysical sterile neutrinos allows an apparent unitarity violation in the three-flavor triangle space.

We re-examine various design parameters of a neutrino detector at the South Pole. Along with the frequency acceptance of the detector, the sensitivity of an ARA-like detector depends on several parameters such as station depth, station horizontal baseline and geometry of the station. The effect of some of these parameters on sensitivity exhibit significant energy dependence. We also analyze the change in signal response of the station with different frequency acceptance bands which could help reduce the cost of the new upcoming stations.