arXiv subjects

Rishab Nithyanand

Publications and source records attributed to Rishab Nithyanand.

33 records · Page 2Linked to original sources

arXiv2021

Are Proactive Interventions for Reddit Communities Feasible?

Reddit has found its communities playing a prominent role in originating and propagating problematic socio-political discourse. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.

cs.SI↗

arXiv2021

Reddit and the Fourth Estate: Exploring the magnitude and effects of media influence on community level moderation on Reddit

Most platforms, including Reddit, face a dilemma when applying interventions such as subreddit bans to toxic communities -- do they risk angering their user base by proactively enforcing stricter controls on discourse or do they defer interventions at the risk of eventually triggering negative media reactions which might impact their advertising revenue? In this paper, we analyze Reddit's previous administrative interventions to understand one aspect of this dilemma: the relationship between the media and administrative interventions. More specifically, we make two primary contributions. First, using a mediation analysis framework, we find evidence that Reddit's interventions for violating their content policy for toxic content occur because of media pressure. Second, using interrupted time series analysis, we show that media attention on communities with toxic content only increases the problematic behavior associated with that community (both within the community itself and across the platform). However, we find no significant difference in the impact of administrative interventions on subreddits with and without media pressure. Taken all together, this study provides evidence of a media-driven moderation strategy at Reddit and also suggests that such a strategy may not have a significantly different impact than a more proactive strategy.

cs.SI↗

arXiv2019

Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding

Online advertising relies on trackers and data brokers to show targeted ads to users. To improve targeting, different entities in the intricately interwoven online advertising and tracking ecosystems are incentivized to share information with each other through client-side or server-side mechanisms. Inferring data sharing between entities, especially when it happens at the server-side, is an important and challenging research problem. In this paper, we introduce KASHF: a novel method to infer data sharing relationships between advertisers and trackers by studying how an advertiser's bidding behavior changes as we manipulate the presence of trackers. We operationalize this insight by training an interpretable machine learning model that uses the presence of trackers as features to predict the bidding behavior of an advertiser. By analyzing the machine learning model, we are able to infer relationships between advertisers and trackers irrespective of whether data sharing occurs at the client-side or the server-side. We are also able to identify several server-side data sharing relationships that are validated externally but are not detected by client-side cookie syncing.

cs.CY↗

arXiv2019

To Act or React: Investigating Proactive Strategies For Online Community Moderation

Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, as shown in our background research, administrative actions (community bans and quarantines) are often taken in reaction to media pressure following offensive discourse within a community spilling into the real world with serious consequences. In this paper, we investigate the feasibility of proactive moderation on Reddit -- i.e., proactively identifying communities at risk of committing offenses that previously resulted in bans for other communities. Proactive moderation strategies show promise for two reasons: (1) they have potential to narrow down the communities that administrators need to monitor for hateful content and (2) they give administrators a scientific rationale to back their administrative decisions and interventions. Our work shows that communities are constantly evolving in their user base and topics of discourse and that evolution into hateful or dangerous (i.e., considered bannable by Reddit administrators) communities can often be predicted months ahead of time. This makes proactive moderation feasible. Further, we leverage explainable machine learning to help identify the strongest predictors of evolution into dangerous communities. This provides administrators with insights into the characteristics of communities at risk becoming dangerous or hateful. Finally, we investigate, at scale, the impact of participation in hateful and dangerous subreddits and the effectiveness of community bans and quarantines on the behavior of members of these communities.

cs.SI↗

arXiv2017

Online Political Discourse in the Trump Era

We identify general trends in the (in)civility and complexity of political discussions occurring on Reddit between January 2007 and May 2017 -- a period spanning both terms of Barack Obama's presidency and the first 100 days of Donald Trump's presidency. We then investigate four factors that are frequently hypothesized as having contributed to the declining quality of American political discourse -- (1) the rising popularity of Donald Trump, (2) increasing polarization and negative partisanship, (3) the democratization of news media and the rise of fake news, and (4) merging of fringe groups into mainstream political discussions.

cs.CY↗

arXiv2017

Measuring Offensive Speech in Online Political Discourse

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 -- a period covering several divisive political events including the 2016 US presidential elections.

cs.CL↗

arXiv2017

A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies.

cs.CR↗

arXiv2016

Exploring the Design Space of Longitudinal Censorship Measurement Platforms

Despite the high perceived value and increasing severity of online information controls, a data-driven understanding of the phenomenon has remained elusive. In this paper, we consider two design points in the space of Internet censorship measurement with particular emphasis on how they address the challenges of locating vantage points, choosing content to test, and analyzing results. We discuss the trade offs of decisions made by each platform and show how the resulting data provides complementary views of global censorship. Finally, we discuss lessons learned and open challenges discovered through our experiences.

cs.NI↗

arXiv2016

Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem

Third-party services form an integral part of the mobile ecosystem: they allow app developers to add features such as performance analytics and social network integration, and to monetize their apps by enabling user tracking and targeted ad delivery. At present users, researchers, and regulators all have at best limited understanding of this third-party ecosystem. In this paper we seek to shrink this gap. Using data from users of our ICSI Haystack app we gain a rich view of the mobile ecosystem: we identify and characterize domains associated with mobile advertising and user tracking, thereby taking an important step towards greater transparency. We furthermore outline our steps towards a public catalog and census of analytics services, their behavior, their personal data collection processes, and their use across mobile apps.

cs.CY↗

arXiv2016

Ad-Blocking and Counter Blocking: A Slice of the Arms Race

Adblocking tools like Adblock Plus continue to rise in popularity, potentially threatening the dynamics of advertising revenue streams. In response, a number of publishers have ramped up efforts to develop and deploy mechanisms for detecting and/or counter-blocking adblockers (which we refer to as anti-adblockers), effectively escalating the online advertising arms race. In this paper, we develop a scalable approach for identifying third-party services shared across multiple web-sites and use it to provide a first characterization of anti-adblocking across the Alexa Top-5K websites. We map websites that perform anti-adblocking as well as the entities that provide anti-adblocking scripts. We study the modus operandi of these scripts and their impact on popular adblockers. We find that at least 6.7% of websites in the Alexa Top-5K use anti-adblocking scripts, acquired from 12 distinct entities -- some of which have a direct interest in nourishing the online advertising industry.

cs.CR↗

arXiv2016

Holding all the ASes: Identifying and Circumventing the Pitfalls of AS-aware Tor Client Design

Traffic correlation attacks to de-anonymize Tor users are possible when an adversary is in a position to observe traffic entering and exiting the Tor network. Recent work has brought attention to the threat of these attacks by network-level adversaries (e.g., Autonomous Systems). We perform a historical analysis to understand how the threat from AS-level traffic correlation attacks has evolved over the past five years. We find that despite a large number of new relays added to the Tor network, the threat has grown. This points to the importance of increasing AS-level diversity in addition to capacity of the Tor network. We identify and elaborate on common pitfalls of AS-aware Tor client design and construction. We find that succumbing to these pitfalls can negatively impact three major aspects of an AS-aware Tor client -- (1) security against AS-level adversaries, (2) security against relay-level adversaries, and (3) performance. Finally, we propose and evaluate a Tor client -- Cipollino -- which avoids these pitfalls using state-of-the-art in network-measurement. Our evaluation shows that Cipollino is able to achieve better security against network-level adversaries while maintaining security against relay-level adversaries and

cs.CR↗

arXiv2015

Measuring and mitigating AS-level adversaries against Tor

The popularity of Tor as an anonymity system has made it a popular target for a variety of attacks. We focus on traffic correlation attacks, which are no longer solely in the realm of academic research with recent revelations about the NSA and GCHQ actively working to implement them in practice. Our first contribution is an empirical study that allows us to gain a high fidelity snapshot of the threat of traffic correlation attacks in the wild. We find that up to 40% of all circuits created by Tor are vulnerable to attacks by traffic correlation from Autonomous System (AS)-level adversaries, 42% from colluding AS-level adversaries, and 85% from state-level adversaries. In addition, we find that in some regions (notably, China and Iran) there exist many cases where over 95% of all possible circuits are vulnerable to correlation attacks, emphasizing the need for AS-aware relay-selection. To mitigate the threat of such attacks, we build Astoria--an AS-aware Tor client. Astoria leverages recent developments in network measurement to perform path-prediction and intelligent relay selection. Astoria reduces the number of vulnerable circuits to 2% against AS-level adversaries, under 5% against colluding AS-level adversaries, and 25% against state-level adversaries. In addition, Astoria load balances across the Tor network so as to not overload any set of relays.

cs.CR↗

arXiv2015

Games Without Frontiers: Investigating Video Games as a Covert Channel

The Internet has become a critical communication infrastructure for citizens to organize protests and express dissatisfaction with their governments. This fact has not gone unnoticed, with governments clamping down on this medium via censorship, and circumvention researchers working to stay one step ahead. In this paper, we explore a promising new avenue for covert channels: real-time strategy-video games. Video games have two key features that make them attractive cover protocols for censorship circumvention. First, due to the popularity of gaming platforms such as Steam, there are a lot of different video games, each with their own protocols and server infrastructure. Users of video-game-based censorship-circumvention tools can therefore diversify across many games, making it difficult for the censor to respond by simply blocking a single cover protocol. Second, games in the same genre have many common features and concepts. As a result, the same covert channel framework can be easily adapted to work with many different games. This means that circumvention tool developers can stay ahead of the censor by creating a diverse set of tools and by quickly adapting to blockades created by the censor. We demonstrate the feasibility of this approach by implementing our coding scheme over two real-time strategy-games (including a very popular closed-source game). We evaluate the security of our system prototype -- Castle -- by quantifying its resilience to a censor-adversary, its similarity to real game traffic, and its ability to avoid common pitfalls in covert channel design. We use our prototype to demonstrate that our approach can provide throughput which is amenable to transfer of textual data, such at e-mail, SMS messages, and tweets, which are commonly used to organize political actions.

cs.CR↗

arXiv2014

How Best to Handle a Dicey Situation

We introduce the {Destructive Object Handling} (DOH) problem, which models aspects of many real-world allocation problems, such as shipping explosive munitions, scheduling processes in a cluster with fragile nodes, re-using passwords across multiple websites, and quarantining patients during a disease outbreak. In these problems, objects must be assigned to handlers, but each object has a probability of destroying itself and all the other objects allocated to the same handler. The goal is to maximize the expected value of the objects handled successfully. We show that finding the optimal allocation is $\mathsf{NP}$-$\mathsf{complete}$, even if all the handlers are identical. We present an FPTAS when the number of handlers is constant. We note in passing that the same technique also yields a first FPTAS for the weapons-target allocation problem \cite{manne_wta} with a constant number of targets. We study the structure of DOH problems and find that they have a sort of phase transition -- in some instances it is better to spread risk evenly among the handlers, in others, one handler should be used as a ``sacrificial lamb''. We show that the problem is solvable in polynomial time if the destruction probabilities depend only on the handler to which an object is assigned; if all the handlers are identical and the objects all have the same value; or if each handler can be assigned at most one object. Finally, we empirically evaluate several heuristics based on a combination of greedy and genetic algorithms. The proposed heuristics return fairly high quality solutions to very large problem instances (upto 250 objects and 100 handlers) in tens of seconds.

cs.DS↗

arXiv2014

New Approaches to Website Fingerprinting Defenses

Website fingerprinting attacks enable an adversary to infer which website a victim is visiting, even if the victim uses an encrypting proxy, such as Tor. Previous work has shown that all proposed defenses against website fingerprinting attacks are ineffective. This paper advances the study of website fingerprinting attacks and defenses in two ways. First, we develop bounds on the trade-off between security and bandwidth overhead that any fingerprinting defense scheme can achieve. This enables us to compare schemes with different security/overhead trade-offs by comparing how close they are to the lower bound. We then refine, implement, and evaluate the Congestion Sensitive BuFLO scheme outlined by Cai, et al. CS-BuFLO, which is based on the provably-secure BuFLO defense proposed by Dyer, et al., was not fully-specified by Cai, et al, but has nonetheless attracted the attention of the Tor developers. Our experiments find that CS-BuFLO has high overhead (around 2.3-2.8x) but can get 6x closer to the bandwidth/security trade-off lower bound than Tor or plain SSH.

cs.CR↗