arXiv subjects

Bogdan Carbunar

Publications and source records attributed to Bogdan Carbunar.

At least 19 recordsLinked to original sources

arXiv2025

Cooperative Dynamics of Censorship, Misinformation, and Influence Operations: Insights from the Global South and U.S

Censorship and the distribution of false information, tools used to manipulate what users see and believe, are seemingly at opposite ends of the information access spectrum. Most previous work has examined them in isolation and within individual countries, leaving gaps in our understanding of how these information manipulation tools interact and reinforce each other across diverse societies. In this paper, we study perceptions about the interplay between censorship, false information, and influence operations, gathered through a mixed-methods study consisting of a survey (n = 384) and semi-structured interviews (n = 30) with participants who have experienced these phenomena across diverse countries in both the Global South and Global North, including Bangladesh, China, Cuba, Iran, Venezuela, and the United States. Our findings reveal perceptions of cooperation across various platforms between distinct entities working together to create information cocoons, within which censorship and false information become imperceptible to those affected. Building on study insights, we propose novel platform-level interventions to enhance transparency and help users navigate information manipulation. In addition, we introduce the concept of plausibly deniable social platforms, enabling censored users to provide credible, benign explanations for their activities, protecting them from surveillance and coercion.

cs.HC↗

arXiv2025

Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter

This paper investigates inauthentic duplication on social media, where multiple accounts share identical misinformation tweets. Leveraging a dataset of misinformation verified by AltNews, an Indian fact-checking organization, we analyze over 12 million posts from 5,493 accounts known to have duplicated such content. Contrary to common assumptions that bots are primarily responsible for spreading false information, fewer than 1\% of these accounts exhibit bot-like behavior. We present TweeXster, a framework for detecting and analyzing duplication campaigns, revealing clusters of accounts involved in repeated and sometimes revived dissemination of false or abusive content.

cs.SI↗

arXiv2025

Victim-Centred Abuse Investigations and Defenses for Social Media Platforms

Online abuse, a persistent aspect of social platform interactions, impacts user well-being and exposes flaws in platform designs that include insufficient detection efforts and inadequate victim protection measures. Ensuring safety in platform interactions requires the integration of victim perspectives in the design of abuse detection and response systems. In this paper, we conduct surveys (n = 230) and semi-structured interviews (n = 15) with students at a minority-serving institution in the US, to explore their experiences with abuse on a variety of social platforms, their defense strategies, and their recommendations for social platforms to improve abuse responses. We build on study findings to propose design requirements for abuse defense systems and discuss the role of privacy, anonymity, and abuse attribution requirements in their implementation. We introduce ARI, a blueprint for a unified, transparent, and personalized abuse response system for social platforms that sustainably detects abuse by leveraging the expertise of platform users, incentivized with proceeds obtained from abusers.

cs.CR↗

arXiv2023

THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech

Detecting harmful content on social media, such as Twitter, is made difficult by the fact that the seemingly simple yes/no classification conceals a significant amount of complexity. Unfortunately, while several datasets have been collected for training classifiers in hate and offensive speech, there is a scarcity of datasets labeled with a finer granularity of target classes and specific targets. In this paper, we introduce THOS, a dataset of 8.3k tweets manually labeled with fine-grained annotations about the target of the message. We demonstrate that this dataset makes it feasible to train classifiers, based on Large Language Models, to perform classification at this level of granularity.

cs.CL↗

arXiv2023

A Study of China's Censorship and Its Evasion Through the Lens of Online Gaming

For the past 20 years, China has increasingly restricted the access of minors to online games using addiction prevention systems (APSes). At the same time, and through different means, i.e., the Great Firewall of China (GFW), it also restricts general population access to the international Internet. This paper studies how these restrictions impact young online gamers, and their evasion efforts. We present results from surveys (n = 2,415) and semi-structured interviews (n = 35) revealing viable commonly deployed APS evasion techniques and APS vulnerabilities. We conclude that the APS does not work as designed, even against very young online game players, and can act as a censorship evasion training ground for tomorrow's adults, by familiarization with and normalization of general evasion techniques, and desensitization to their dangers. Findings from these studies may further inform developers of censorship-resistant systems about the perceptions and evasion strategies of their prospective users, and help design tools that leverage services and platforms popular among the censored audience.

cs.CY↗

arXiv2022

Strategies and Vulnerabilities of Participants in Venezuelan Influence Operations

Studies of online influence operations, coordinated efforts to disseminate and amplify disinformation, focus on forensic analysis of social networks or of publicly available datasets of trolls and bot accounts. However, little is known about the experiences and challenges of human participants in influence operations. We conducted semi-structured interviews with 19 influence operations participants that contribute to the online image of Venezuela, to understand their incentives, capabilities, and strategies to promote content while evading detection. To validate a subset of their answers, we performed a quantitative investigation using data collected over almost four months, from Twitter accounts they control. We found diverse participants that include pro-government and opposition supporters, operatives and grassroots campaigners, and sockpuppet account owners and real users. While pro-government and opposition participants have similar goals and promotion strategies, they differ in their motivation, organization, adversaries and detection avoidance strategies. We report the Patria framework, a government platform for operatives to log activities and receive benefits. We systematize participant strategies to promote political content, and to evade and recover from Twitter penalties. We identify vulnerability points associated with these strategies, and suggest more nuanced defenses against influence operations.

cs.SI↗

arXiv2021

SoK: Plausibly Deniable Storage

Data privacy is critical in instilling trust and empowering the societal pacts of modern technology-driven democracies. Unfortunately, it is under continuous attack by overreaching or outright oppressive governments, including some of the world's oldest democracies. Increasingly-intrusive anti-encryption laws severely limit the ability of standard encryption to protect privacy. New defense mechanisms are needed. Plausible deniability (PD) is a powerful property, enabling users to hide the existence of sensitive information in a system under direct inspection by adversaries. Popular encrypted storage systems such as TrueCrypt and other research efforts have attempted to also provide plausible deniability. Unfortunately, these efforts have often operated under less well-defined assumptions and adversarial models. Careful analyses often uncover not only high overheads but also outright security compromise. Further, our understanding of adversaries, the underlying storage technologies, as well as the available plausible deniable solutions have evolved dramatically in the past two decades. The main goal of this work is to systematize this knowledge. It aims to: - identify key PD properties, requirements, and approaches; - present a direly-needed unified framework for evaluating security and performance; - explore the challenges arising from the critical interplay between PD and modern system layered stacks; - propose a new "trace-oriented" PD paradigm, able to decouple security guarantees from the underlying systems and thus ensure a higher level of flexibility and security independent of the technology stack. This work is meant also as a trusted guide for system and security practitioners around the major challenges in understanding, designing, and implementing plausible deniability into new or existing systems.

cs.CR↗

arXiv2021

RacketStore: Measurements of ASO Deception in Google Play via Mobile and App Usage

Online app search optimization (ASO) platforms that provide bulk installs and fake reviews for paying app developers in order to fraudulently boost their search rank in app stores, were shown to employ diverse and complex strategies that successfully evade state-of-the-art detection methods. In this paper we introduce RacketStore, a platform to collect data from Android devices of participating ASO providers and regular users, on their interactions with apps which they install from the Google Play Store. We present measurements from a study of 943 installs of RacketStore on 803 unique devices controlled by ASO providers and regular users, that consists of 58,362,249 data snapshots collected from these devices, the 12,341 apps installed on them and their 110,511,637 Google Play reviews. We reveal significant differences between ASO providers and regular users in terms of the number and types of user accounts registered on their devices, the number of apps they review, and the intervals between the installation times of apps and their review times. We leverage these insights to introduce features that model the usage of apps and devices, and show that they can train supervised learning algorithms to detect paid app installs and fake reviews with an F1-measure of 99.72% (AUC above 0.99), and detect devices controlled by ASO providers with an F1-measure of 95.29% (AUC = 0.95). We discuss the costs associated with evading detection by our classifiers and also the potential for app stores to use our approach to detect ASO work with privacy.

cs.CR↗

arXiv2021

Toward Uncensorable, Anonymous and Private Access Over Satoshi Blockchains

Providing unrestricted access to sensitive content such as news and software is difficult in the presence of adaptive and resourceful surveillance and censoring adversaries. In this paper we leverage the distributed and resilient nature of commercial Satoshi blockchains to develop the first provably secure, censorship resistant, cost-efficient storage system with anonymous and private access, built on top of commercial cryptocurrency transactions. We introduce max-rate transactions, a practical construct to persist data of arbitrary size entirely in a Satoshi blockchain. We leverage max-rate transactions to develop UWeb, a blockchain-based storage system that charges publishers to self-sustain its decentralized infrastructure. UWeb organizes blockchainstored content for easy retrieval, and enables clients to store and access content with provable anonymity, privacy and censorship resistance properties. We present results from UWeb experiments with writing 268.21 MB of data into the live Litecoin blockchain, including 4.5 months of live-feed BBC articles, and 41 censorship resistant tools. The max-rate writing throughput (183 KB/s) and blockchain utilization (88%) exceed those of state-of-the-art solutions by 2-3 orders of magnitude and broke Litecoin's record of the daily average block size. Our simulations with up to 3,000 concurrent UWeb writers confirm that UWeb does not impact the confirmation delays of financial transactions.

cs.CR↗

arXiv2019

Private and Atomic Exchange of Assets over Zero Knowledge Based Payment Ledger

Bitcoin brings a new type of digital currency that does not rely on a central system to maintain transactions. By benefiting from the concept of decentralized ledger, users who do not know or trust each other can still conduct transactions in a peer-to-peer manner. Inspired by Bitcoin, other cryptocurrencies were invented in recent years such as Ethereum, Dash, Zcash, Monero, Grin, etc. Some of these focus on enhancing privacy for instance crypto note or systems that apply the similar concept of encrypted notes used for transactions to enhance privacy (e.g., Zcash, Monero). However, there are few mechanisms to support the exchange of privacy-enhanced notes or assets on the chain, and at the same time preserving the privacy of the exchange operations. Existing approaches for fair exchanges of assets with privacy mostly rely on off-chain/side-chain, escrow or centralized services. Thus, we propose a solution that supports oblivious and privacy-protected fair exchange of crypto notes or privacy enhanced crypto assets. The technology is demonstrated by extending zero-knowledge based crypto notes. To address "privacy" and "multi-currency", we build a new zero-knowledge proving system and extend note format with new property to represent various types of tokenized assets or cryptocurrencies. By extending the payment protocol, exchange operations are realized through privacy enhanced transactions (e.g., shielded transactions). Based on the possible scenarios during the exchange operation, we add new constraints and conditions to the zero-knowledge proving system used for validating transactions publicly.

cs.CR↗

arXiv2018

Tithonus: A Bitcoin Based Censorship Resilient System

Providing reliable and surreptitious communications is difficult in the presence of adaptive and resourceful state level censors. In this paper we introduce Tithonus, a framework that builds on the Bitcoin blockchain and network to provide censorship-resistant communication mechanisms. In contrast to previous approaches, we do not rely solely on the slow and expensive blockchain consensus mechanism but instead fully exploit Bitcoin's peer-to-peer gossip protocol. We develop adaptive, fast and cost effective data communication solutions that camouflage client requests into inconspicuous Bitcoin transactions. We propose solutions to securely request and transfer content, with unobservability and censorship resistance, and free, pay-per-access and subscription based payment options. When compared to state-of-the-art Bitcoin writing solutions, Tithonus reduces the cost of transferring data to censored clients by 2 orders of magnitude and increases the goodput by 3 to 5 orders of magnitude. We show that Tithonus client initiated transactions are hard to detect, while server initiated transactions cannot be censored without creating split world problems to the Bitcoin blockchain.

cs.CR↗

arXiv2018

A Secure Mobile Authentication Alternative to Biometrics

Biometrics are widely used for authentication in consumer devices and business settings as they provide sufficiently strong security, instant verification and convenience for users. However, biometrics are hard to keep secret, stolen biometrics pose lifelong security risks to users as they cannot be reset and re-issued, and transactions authenticated by biometrics across different systems are linkable and traceable back to the individual identity. In addition, their cost-benefit analysis does not include personal implications to users, who are least prepared for the imminent negative outcomes, and are not often given equally convenient alternative authentication options. We introduce ai.lock, a secret image based authentication method for mobile devices which uses an imaging sensor to reliably extract authentication credentials similar to biometrics. Despite lacking the regularities of biometric image features, we show that ai.lock consistently extracts features across authentication attempts from general user captured images, to reconstruct credentials that can match and exceed the security of biometrics (EER = 0.71%). ai.lock only stores a hash of the object's image. We measure the security of ai.lock against brute force attacks on more than 3.5 billion authentication instances built from more than 250,000 images of real objects, and 100,000 synthetically generated images using a generative adversarial network trained on object images. We show that the ai.lock Shannon entropy is superior to a fingerprint based authentication built into popular mobile devices.

cs.CR↗

arXiv2018

Search Rank Fraud De-Anonymization in Online Systems

We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.

cs.SI↗

arXiv2018

AbuSniff: Automatic Detection and Defenses Against Abusive Facebook Friends

Adversaries leverage social network friend relationships to collect sensitive data from users and target them with abuse that includes fake news, cyberbullying, malware, and propaganda. Case in point, 71 out of 80 user study participants had at least 1 Facebook friend with whom they never interact, either in Facebook or in real life, or whom they believe is likely to abuse their posted photos or status updates, or post offensive, false or malicious content. We introduce AbuSniff, a system that identifies Facebook friends perceived as strangers or abusive, and protects the user by unfriending, unfollowing, or restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse.We introduce mutual Facebook activity features and show that they can train supervised learning algorithms to predict questionnaire responses. We have evaluated AbuSniff through several user studies with a total of 263 participants from 25 countries. After answering the questionnaire, participants agreed to unfollow and restrict abusers in 91.6% and 90.9% of the cases respectively, and sandbox or unfriend non-abusive strangers in 92.45% of the cases. Without answering the questionnaire, participants agreed to take the AbuSniff suggested action against friends predicted to be strangers or abusive, in 78.2% of the cases. AbuSniff increased the participant self-reported willingness to reject invitations from strangers and abusers, their awareness of friend abuse implications and their perceived protection from friend abuse.

cs.SI↗

arXiv2018

A Longitudinal Study of Google Play

The difficulty of large scale monitoring of app markets affects our understanding of their dynamics. This is particularly true for dimensions such as app update frequency, control and pricing, the impact of developer actions on app popularity, as well as coveted membership in top app lists. In this paper we perform a detailed temporal analysis on two datasets we have collected from the Google Play Store, one consisting of 160,000 apps and the other of 87,223 newly released apps. We have monitored and collected data about these apps over more than 6 months. Our results show that a high number of these apps have not been updated over the monitoring interval. Moreover, these apps are controlled by a few developers that dominate the total number of app downloads. We observe that infrequently updated apps significantly impact the median app price. However, a changing app price does not correlate with the download count. Furthermore, we show that apps that attain higher ranks have better stability in top app lists. We show that app market analytics can help detect emerging threat vectors, and identify search rank fraud and even malware. Further, we discuss the research implications of app market analytics on improving developer and user experiences.

cs.SI↗

arXiv2017

Camera Based Two Factor Authentication Through Mobile and Wearable Devices

We introduce Pixie, a novel, camera based two factor authentication solution for mobile and wearable devices. A quick and familiar user action of snapping a photo is sufficient for Pixie to simultaneously perform a graphical password authentication and a physical token based authentication, yet it does not require any expensive, uncommon hardware. Pixie establishes trust based on both the knowledge and possession of an arbitrary physical object readily accessible to the user, called trinket. Users choose their trinkets similar to setting a password, and authenticate by presenting the same trinket to the camera. The fact that the object is the trinket, is secret to the user. Pixie extracts robust, novel features from trinket images, and leverages a supervised learning classifier to effectively address inconsistencies between images of the same trinket captured in different circumstances. Pixie achieved a false accept rate below 0.09% in a brute force attack with 14.3 million authentication attempts, generated with 40,000 trinket images that we captured and collected from public datasets. We identify master images, that match multiple trinkets, and study techniques to reduce their impact. In a user study with 42 participants over 8 days in 3 sessions we found that Pixie outperforms text based passwords on memorability, speed, and user preference. Furthermore, Pixie was easily discoverable by new users and accurate under field use. Users were able to remember their trinkets 2 and 7 days after registering them, without any practice between the 3 test dates.

cs.CR↗

arXiv2017

Stateless Puzzles for Real Time Online Fraud Preemption

The profitability of fraud in online systems such as app markets and social networks marks the failure of existing defense mechanisms. In this paper, we propose FraudSys, a real-time fraud preemption approach that imposes Bitcoin-inspired computational puzzles on the devices that post online system activities, such as reviews and likes. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles, that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph-based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. FraudSys does not alter the experience of users in online systems, but delays fraudulent actions and consumes significant computational resources of the fraudsters. Using real datasets from Google Play and Facebook, we demonstrate the feasibility of FraudSys by showing that the devices of honest users are minimally impacted, while fraudster controlled devices receive daily computational penalties of up to 3,079 hours. In addition, we show that with FraudSys, fraud does not pay off, as a user equipped with mining hardware (e.g., AntMiner S7) will earn less than half through fraud than from honest Bitcoin mining.

cs.SI↗

arXiv2017

Video Liveness for Citizen Journalism: Attacks and Defenses

The impact of citizen journalism raises important video integrity and credibility issues. In this article, we introduce Vamos, the first user transparent video "liveness" verification solution based on video motion, that accommodates the full range of camera movements, and supports videos of arbitrary length. Vamos uses the agreement between video motion and camera movement to corroborate the video authenticity. Vamos can be integrated into any mobile video capture application without requiring special user training. We develop novel attacks that target liveness verification solutions. The attacks leverage both fully automated algorithms and trained human experts. We introduce the concept of video motion categories to annotate the camera and user motion characteristics of arbitrary videos. We show that the performance of Vamos depends on the video motion category. Even though Vamos uses motion as a basis for verification, we observe a surprising and seemingly counter-intuitive resilience against attacks performed on relatively "stationary" video chunks, which turn out to contain hard-to-imitate involuntary movements. We show that overall the accuracy of Vamos on the task of verifying whole length videos exceeds 93\% against the new attacks.

cs.CR↗