arXiv subjects

Nikita Samarin

Publications and source records attributed to Nikita Samarin.

8 recordsLinked to original sources

arXiv2024

The Medium is the Message: How Secure Messaging Apps Leak Sensitive Data to Push Notification Services

Like most modern software, secure messaging apps rely on third-party components to implement important app functionality. Although this practice reduces engineering costs, it also introduces the risk of inadvertent privacy breaches due to misconfiguration errors or incomplete documentation. Our research investigated secure messaging apps' usage of Google's Firebase Cloud Messaging (FCM) service to send push notifications to Android devices. We analyzed 21 popular secure messaging apps from the Google Play Store to determine what personal information these apps leak in the payload of push notifications sent via FCM. Of these apps, 11 leaked metadata, including user identifiers (10 apps), sender or recipient names (7 apps), and phone numbers (2 apps), while 4 apps leaked the actual message content. Furthermore, none of the data we observed being leaked to FCM was specifically disclosed in those apps' privacy disclosures. We also found several apps employing strategies to mitigate this privacy leakage to FCM, with varying levels of success. Of the strategies we identified, none appeared to be common, shared, or well-supported. We argue that this is fundamentally an economics problem: incentives need to be correctly aligned to motivate platforms and SDK providers to make their systems secure and private by default.

cs.CR↗

arXiv2024

Privacy Engineering From Principles to Practice: A Roadmap

Privacy engineering is gaining momentum in industry and academia alike. So far, manifold low-level primitives and higher-level methods and strategies have successfully been established. Still, fostering adoption in real-world information systems calls for additional aspects to be consciously considered in research and practice.

cs.CR↗

arXiv2023

Lessons in VCR Repair: Compliance of Android App Developers with the California Consumer Privacy Act (CCPA)

The California Consumer Privacy Act (CCPA) provides California residents with a range of enhanced privacy protections and rights. Our research investigated the extent to which Android app developers comply with the provisions of the CCPA that require them to provide consumers with accurate privacy notices and respond to "verifiable consumer requests" (VCRs) by disclosing personal information that they have collected, used, or shared about consumers for a business or commercial purpose. We compared the actual network traffic of 109 apps that we believe must comply with the CCPA to the data that apps state they collect in their privacy policies and the data contained in responses to "right to know" requests that we submitted to the app's developers. Of the 69 app developers who substantively replied to our requests, all but one provided specific pieces of personal data (as opposed to only categorical information). However, a significant percentage of apps collected information that was not disclosed, including identifiers (55 apps, 80%), geolocation data (21 apps, 30%), and sensory data (18 apps, 26%) among other categories. We discuss improvements to the CCPA that could help app developers comply with "right to know" requests and other related regulations.

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arXiv2022

Examining the Landscape of Digital Safety and Privacy Assistance for Black Communities

Recent events have placed a renewed focus on the issue of racial justice in the United States and other countries. One dimension of this issue that has received considerable attention is the security and privacy threats and vulnerabilities faced by the communities of color. Our study focuses on community-level advocates who organize workshops, clinics, and other initiatives that inform Black communities about existing digital safety and privacy threats and ways to mitigate against them. Additionally, we aim to understand the online security and privacy needs and attitudes of participants who partake in these initiatives. We hope that by understanding how advocates work in different contexts and what teaching methods are effective, we can help other digital safety experts and activists become advocates within their communities.

cs.CY↗

arXiv2020

Surveying Vulnerable Populations: A Case Study of Civil Society Organizations

Compared to organizations in other sectors, civil society organizations (CSOs) are particularly vulnerable to security and privacy threats, as they lack adequate resources and expertise to defend themselves. At the same time, their security needs and practices have not gained much attention among researchers, and existing solutions designed for the average users do not consider the contexts in which CSO employees operate. As part of our preliminary work, we conducted an anonymous online survey with 102 CSO employees to collect information about their perceived risks of different security and privacy threats, and their self-reported mitigation strategies. The design of our preliminary survey accounted for the unique requirements of our target population by establishing trust with respondents, using anonymity-preserving incentive strategies, and distributing the survey with the help of a trusted intermediary. However, by carefully examining our methods and the feedback received from respondents, we uncovered several issues with our methodology, including the length of the survey, the framing of the questions, and the design of the recruitment email. We hope that the discussion presented in this paper will inform and assist researchers and practitioners working on understanding and improving the security and privacy of CSOs.

cs.CY↗

arXiv2019

Evading classifiers in discrete domains with provable optimality guarantees

Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against adversarial examples. The existing literature on provable adversarial robustness of models, however, exclusively focuses on robustness to gradient-based attacks in domains such as images. These attacks model the adversarial cost, e.g., amount of distortion applied to an image, as a $p$-norm. We argue that this approach is not well-suited to model adversarial costs in constrained domains where not all examples are feasible. We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost. These guarantees directly translate into a notion of adversarial robustness that takes into account domain constraints and the adversary's capabilities. We show how our framework can be used to evaluate security by crafting adversarial examples that evade a Twitter-bot detection classifier with provably minimal number of changes; and to build privacy defenses by crafting adversarial examples that evade a privacy-invasive website-fingerprinting classifier.

cs.LG↗

arXiv2019

A Key to Your Heart: Biometric Authentication Based on ECG Signals

In recent years, there has been a shift of interest towards the field of biometric authentication, which proves the identity of the user using their biological characteristics. We explore a novel biometric based on the electrical activity of the human heart in the form of electrocardiogram (ECG) signals. In order to explore the stability of ECG as a biometric, we collect data from 55 participants over two sessions with a period of 4 months in between. We also use a consumer-grade ECG monitor that is more affordable and usable than a medical-grade counterpart. Using a standard approach to evaluate our classifier, we obtain error rates of 2.4% for data collected within one session and 9.7% for data collected across two sessions. The experimental results suggest that ECG signals collected using a consumer-grade monitor can be successfully used for user authentication.

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arXiv2019

PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.

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