Generative models producing synthetic data are meant to provide a privacy-friendly approach to releasing data. However, their privacy guarantees are only considered robust when models satisfy Differential Privacy (DP). Alas, this is not a ubiquitous standard, as many leading companies (and, in fact, research papers) use ad-hoc privacy metrics based on testing the statistical similarity between synthetic and real data. In this paper, we examine the privacy metrics used in real-world synthetic data deployments and demonstrate their unreliability in several ways. First, we provide counter-examples where severe privacy violations occur even if the privacy tests pass and instantiate accurate membership and attribute inference attacks with minimal cost. We then introduce Recon-Syn, a reconstruction attack that generates multiple synthetic datasets that are considered private by the metrics but actually leak information unique to individual records. We show that ReconSyn recovers 78-100% of the outliers in the train data with only black-box access to a single fitted generative model and the privacy metrics. In the process, we show that applying DP only to the model does not mitigate this attack, as using privacy metrics breaks the end-to-end DP pipeline.

Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions. This raises concerns about the accuracy of current understandings of real-world fingerprinting deployments. As a result, this paper presents a user study involving 30 participants over 10 weeks, capturing telemetry data from real browsing sessions across 3,000 top-ranked websites. Our evaluation reveals that automated crawls miss almost half (45%) of the fingerprinting websites encountered by real users. This discrepancy mainly stems from the crawlers' inability to access authentication-protected pages, circumvent bot detection, and trigger fingerprinting scripts activated by specific user interactions. We also identify potential new fingerprinting vectors present in real user data but absent from automated crawls. Finally, we evaluate the effectiveness of federated learning for training browser fingerprinting detection models on real user data, yielding improved performance than models trained solely on automated crawl data.

CCS ’24, October 14–18, 2024, Salt Lake City, UT, USA.

Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts.

Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.

This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work. The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agnostic to the choice of the initial model parameters. For models trained on MNIST and CIFAR-10 at theoretical $\varepsilon=10.0$, our auditing procedure yields empirical estimates of $\varepsilon_{emp} = 7.21$ and $6.95$, respectively, on a 1,000-record sample and $\varepsilon_{emp} = 6.48$ and $4.96$ on the full datasets. By contrast, previous audits were only (relatively) tight in stronger white-box models, where the adversary can access the model's inner parameters and insert arbitrary gradients. Overall, our auditing procedure can offer valuable insight into how the privacy analysis of DP-SGD could be improved and detect bugs and DP violations in real-world implementations. The source code needed to reproduce our experiments is available from https://github.com/spalabucr/bb-audit-dpsgd.

Federated learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their data; rather, they train their own model locally and send updates to a central server for aggregation. Depending on how the data is distributed among the participants, FL can be classified into Horizontal (HFL) and Vertical (VFL). In VFL, the participants share the same set of training instances but only host a different and non-overlapping subset of the whole feature space. Whereas in HFL, each participant shares the same set of features while the training set is split into locally owned training data subsets.VFL is increasingly used in applications like financial fraud detection; nonetheless, very little work has analyzed its security. In this paper, we focus on robustness in VFL, in particular, on backdoor attacks, whereby an adversary attempts to manipulate the aggregate model during the training process to trigger misclassifications. Performing backdoor attacks in VFL is more challenging than in HFL, as the adversary i) does not have access to the labels during training and ii) cannot change the labels as she only has access to the feature embeddings. We present a first-of-its-kind clean-label backdoor attack in VFL, which consists of two phases: a label inference and a backdoor phase. We demonstrate the effectiveness of the attack on three different datasets, investigate the factors involved in its success, and discuss countermeasures to mitigate its impact.

The COVID-19 pandemic brought about an extraordinary rate of scientific papers on the topic that were discussed among the general public, although often in biased or misinformed ways. In this paper, we present a mixed-methods analysis aimed at examining whether public discussions were commensurate with the scientific consensus on several COVID-19 issues. We estimate scientific consensus based on samples of abstracts from preprint servers and compare against the volume of public discussions on Twitter mentioning these papers. We find that anti-consensus posts and users, though overall less numerous than pro-consensus ones, are vastly over-represented on Twitter, thus producing a false consensus effect. This transpires with favorable papers being disproportionately amplified, along with an influx of new anti-consensus user sign-ups. Finally, our content analysis highlights that anti-consensus users misrepresent scientific findings or question scientists' integrity in their efforts to substantiate their claims.

Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they comment on other communities. We then create a typology of relationships between political communities based on whether their users are toxic to each other, whether echo chamber-like engagement with these communities has a polarizing effect, and on the communities' political leanings. We observe both the echo chamber and hostile intergroup interaction phenomena, but neither holds universally across communities. Contrary to popular belief, we find that polarizing and toxic speech is more dominant between communities on the same, rather than opposing, sides of the political spectrum, especially on the left; however, this mostly points to the collective targeting of political outgroups.

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.

Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible.

Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This paper presents a first-of-its-kind, large-scale measurement of toxicity in chatbots. We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries. Even more worryingly, some non-toxic queries can trigger toxic responses too. We then set out to design and experiment with an attack, ToxicBuddy, which relies on fine-tuning GPT-2 to generate non-toxic queries that make chatbots respond in a toxic manner. Our extensive experimental evaluation demonstrates that our attack is effective against public chatbot models and outperforms manually-crafted malicious queries proposed by previous work. We also evaluate three defense mechanisms against ToxicBuddy, showing that they either reduce the attack performance at the cost of affecting the chatbot's utility or are only effective at mitigating a portion of the attack. This highlights the need for more research from the computer security and online safety communities to ensure that chatbot models do not hurt their users. Overall, we are confident that ToxicBuddy can be used as an auditing tool and that our work will pave the way toward designing more effective defenses for chatbot safety.

Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis underrepresented classes/subgroups of data, specifically, studying: 1) the size of classes/subgroups in the synthetic data, and 2) the accuracy of classification tasks run on them. We also evaluate the effect of various levels of imbalance and privacy budgets. Our analysis uses three state-of-the-art DP models (PrivBayes, DP-WGAN, and PATE-GAN) and shows that DP yields opposite size distributions in the generated synthetic data. It affects the gap between the majority and minority classes/subgroups; in some cases by reducing it (a "Robin Hood" effect) and, in others, by increasing it (a "Matthew" effect). Either way, this leads to (similar) disparate impacts on the accuracy of classification tasks on the synthetic data, affecting disproportionately more the underrepresented subparts of the data. Consequently, when training models on synthetic data, one might incur the risk of treating different subpopulations unevenly, leading to unreliable or unfair conclusions.

Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild. In this paper, we present TROLLMAGNIFIER, a detection system for troll accounts. Our key observation, based on analysis of known Russian-sponsored troll accounts identified by Reddit, is that they show loose coordination, often interacting with each other to further specific narratives. Therefore, troll accounts controlled by the same actor often show similarities that can be leveraged for detection. TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly. We train TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large dataset of Reddit accounts. Our system identifies 1,248 potential troll accounts; we then provide a multi-faceted analysis to corroborate the correctness of our classification. In particular, 66% of the detected accounts show signs of being instrumented by malicious actors (e.g., they were created on the same exact day as a known troll, they have since been suspended by Reddit, etc.). They also discuss similar topics as the known troll accounts and exhibit temporal synchronization in their activity. Overall, we show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%.

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.

The news ecosystem encompasses a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a measurement pipeline able to identify news articles that discuss the same story and trace how they are shared on multiple online communities. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15.6M unique news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news stories, analyzing how they are discussed, and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find t hat f ringe c ommunities o ften h ave a disproportionate influence o n o ther p latforms w .r.t. p ushing n arratives around certain news, for example, about political elections, immigration, or foreign policy. In fact, fringe communities are seemingly successful in influencing the discussion on false narratives about news events on mainstream social networks.

Decentralising the Web is a desirable but challenging goal. One particular challenge is achieving decentralised content moderation in the face of various adversaries (e.g. trolls). To overcome this challenge, many Decentralised Web (DW) implementations rely on federation policies. Administrators use these policies to create rules that ban or modify content that matches specific rules. This, however, can have unintended consequences for many users. In this paper, we present the first study of federation policies on the DW, their in-the-wild usage, and their impact on users. We identify how these policies may negatively impact "innocent" users and outline possible solutions to avoid this problem in the future.

Online fringe communities offer fertile grounds to users seeking and sharing ideas fueling suspicion of mainstream news and conspiracy theories. Among these, the QAnon conspiracy theory emerged in 2017 on 4chan, broadly supporting the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a global pedophile ring. Simultaneously, governments are thought to be controlled by “puppet masters,” as democratically elected officials serve as a fake showroom of democracy.

While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We provide a framework for understanding the types of harassers that target researchers, the harassment that ensues, and the personal and professional impact on individuals and academic freedom. We then study preventative and remedial strategies available, and the institutions that prevent some of these strategies from being more effective. Finally, we discuss the ethical structures that could facilitate more equitable access to participating in research without serious personal suffering.

Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience. Drawing from research in art theory, psychology, marketing, and neuroscience, we develop a codebook to characterize image memes, and use it to annotate a set of 100 image memes collected from 4chan's Politically Incorrect Board (/pol/). On the one hand, we find that highly viral memes are more likely to use a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users. We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared, obtaining an AUC of 0.866 on our dataset. We also show that the indicators of virality identified by our model can help characterize the most viral memes posted on mainstream online social networks too, as our classifiers are able to predict 19 out of the 20 most popular image memes posted on Twitter and Reddit between 2016 and 2018. Overall, our analysis sheds light on what indicators characterize viral and non-viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention.

YouTube is by far the largest host of user-generated video content worldwide. Alas, the platform has also come under fire for hosting inappropriate, toxic, and hateful content. One community that has often been linked to sharing and publishing hateful and misogynistic content are the Involuntary Celibates (Incels), a loosely defined movement ostensibly focusing on men's issues. In this paper, we set out to analyze the Incel community on YouTube by focusing on this community's evolution over the last decade and understanding whether YouTube's recommendation algorithm steers users towards Incel-related videos. We collect videos shared on Incel communities within Reddit and perform a data-driven characterization of the content posted on YouTube. Among other things, we find that the Incel community on YouTube is getting traction and that, during the last decade, the number of Incel-related videos and comments rose substantially. We also find that users have a 6.3% chance of being suggested an Incel-related video by YouTube's recommendation algorithm within five hops when starting from a non Incel-related video. Overall, our findings paint an alarming picture of online radicalization: not only Incel activity is increasing over time, but platforms may also play an active role in steering users towards such extreme content.

When toxic online communities on mainstream platforms face moderation measures, such as bans, they may migrate to other platforms with laxer policies or set up their own dedicated websites. Previous work suggests that within mainstream platforms, community-level moderation is effective in mitigating the harm caused by the moderated communities. It is, however, unclear whether these results also hold when considering the broader Web ecosystem. Do toxic communities continue to grow in terms of their user base and activity on the new platforms? Do their members become more toxic and ideologically radicalized? In this paper, we report the results of a large-scale observational study of how problematic online communities progress following community-level moderation measures. We analyze data from r/The_Donald and r/Incels, two communities that were banned from Reddit and subsequently migrated to their own standalone websites. Our results suggest that, in both cases, moderation measures significantly decreased posting activity on the new platform, reducing the number of posts, active users, and newcomers. In spite of that, users in one of the studied communities (r/The_Donald) showed increases in signals associated with toxicity and radicalization, which justifies concerns that the reduction in activity may come at the expense of a more toxic and radical community. Overall, our results paint a nuanced portrait of the consequences of community-level moderation and can inform their design and deployment.

Recent progress in genomics has enabled the emergence of a flourishing market for direct-to-consumer (DTC) genetic testing. Companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed on social media. At the same time, far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic “purity.” In this article, we set to study the genetic testing discourse on a number of mainstream and fringe Web communities. We do so in two steps. First, we conduct an exploratory, large-scale analysis of the genetic testing discourse on a mainstream social network such as Twitter. We find that the genetic testing discourse is fueled by accounts that appear to be interested in digital health and technology. However, we also identify tweets with highly racist connotations. This motivates us to explore the connection between genetic testing and racism on platforms with a reputation for toxicity, namely, Reddit and 4chan, where we find that discussions around genetic testing often include highly toxic language expressed through hateful and racist comments. In particular, on 4chan’s politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly anti-semitic rhetoric, often conveyed through memes.

The Decentralised Web (DW) has recently seen a renewed momentum, with a number of DW platforms like Mastodon, PeerTube, and Hubzilla gaining increasing traction. These offer alternatives to traditional social networks like Twitter, YouTube, and Facebook, by enabling the operation of web infrastructure and services without centralised ownership or control. Although their services differ greatly, modern DW platforms mostly rely on two key innovations: first, their open source software allows anybody to setup independent servers ("instances") that people can sign-up to and use within a local community; and second, they build on top of federation protocols so that instances can mesh together, in a peer-to-peer fashion, to offer a globally integrated platform.

Collaborative machine learning and related techniques such as federated learning allow multiple participants, each with his own training dataset, to build a joint model by training locally and periodically exchanging model updates. We demonstrate that these updates leak unintended information about participants' training data and develop passive and active inference attacks to exploit this leakage. First, we show that an adversarial participant can infer the presence of exact data points -- for example, specific locations -- in others' training data (i.e., membership inference). Then, we show how this adversary can infer properties that hold only for a subset of the training data and are independent of the properties that the joint model aims to capture. For example, he can infer when a specific person first appears in the photos used to train a binary gender classifier. We evaluate our attacks on a variety of tasks, datasets, and learning configurations, analyze their limitations, and discuss possible defenses.

Search engines play an important role on the Web, helping users find relevant resources and answers to their questions. At the same time, search logs can also be of great utility to researchers. For instance, a number of recent research efforts have relied on them to build prediction and inference models, for applications ranging from economics and marketing to public health surveillance. However, companies rarely release search logs, also due to the related privacy issues that ensue, as they are inherently hard to anonymize. As a result, it is very difficult for researchers to have access to search data, and even if they do, they are fully dependent on the company providing them. Aiming to overcome these issues, this paper presents Private Data Donor (PDD), a decentralized and private-by-design platform providing crowd-sourced Web searches to researchers. We build on a cryptographic protocol for privacy-preserving data aggregation, and address a few practical challenges to add reliability into the system with regards to users disconnecting or stopping using the platform. We discuss how PDD can be used to build a flu monitoring model, and evaluate the impact of the privacy-preserving layer on the quality of the results. Finally, we present the implementation of our platform, as a browser extension and a server, and report on a pilot deployment with real users.

In this paper, we present the design and implementation of SplitBox, a system for privacy-preserving processing of network functions outsourced to cloud middleboxes—i.e., without revealing the policies governing these functions. SplitBox is built to provide privacy for a generic network function that abstracts the functionality of a variety of network functions and associated policies, including firewalls, virtual LANs, network address translators (NATs), deep packet inspection , and load balancers. We present a scalable design aiming to provide high throughput and low latency, by distributing functionalities to a few virtual machines (VMs), while providing provably secure guarantees. We implement SplitBox inside FastClick, an extension of the Click modular router, using Intel’s DPDK to handle packet I/O. We evaluate our prototype experimentally to find its bottlenecks and stress-test its different components, vis-à-vis two widely used network functions, i.e., firewall and VLAN tagging. Our evaluation shows that, on commodity hardware, SplitBox can process packets close to line rate (i.e., 8.9Gbps) with up to 50 traversed policies.

Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior work has shown how these attacks often take place as a result of "raids," i.e., organized efforts by ad-hoc mobs coordinating from third-party communities. Despite the increasing relevance of this phenomenon, however, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human moderation. In this paper, we propose an automated solution to identify YouTube videos that are likely to be targeted by coordinated harassers from fringe communities like 4chan. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of videos that were targeted by raids. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with very good results (AUC up to 94%). Overall, our work provides an important first step towards deploying proactive systems to detect and mitigate coordinated hate attacks on platforms like YouTube.

Abstract Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator’s capacity to learn statistical differences in distributions. We present attacks based on both white-box and black-box access to the target model, against several state-of-the-art generative models, over datasets of complex representations of faces (LFW), objects (CIFAR-10), and medical images (Diabetic Retinopathy). We also discuss the sensitivity of the attacks to different training parameters, and their robustness against mitigation strategies, finding that defenses are either ineffective or lead to significantly worse performances of the generative models in terms of training stability and/or sample quality.

Abstract Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy experts.

As Android has become increasingly popular, so has malware targeting it, thus motivating the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce M A M A D ROID , a static-analysis-based system that abstracts app’s API calls to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate M A M A D ROID using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of 6 years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure 2 years after training). We also show that M A M A D ROID remarkably overperforms D ROID APIM INER , a state-of-the-art detection system that relies on the frequency of ( raw ) API calls. Aiming to assess whether M A M A D ROID ’s effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples that include API calls that are equally or more frequently used by benign apps.

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.

Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensive content, and even manipulating the popularity of messages and accounts. Researchers and cybercriminals are involved in an arms race, and new and updated botnets designed to defeat current detection systems are constantly developed, rendering such detection systems obsolete.

This paper presents an analysis of social experiences around wine consumption through the lens of Vivino, a social network for wine enthusiasts with over 26 million users worldwide. We compare users' perceptions of various wine types and regional styles across both New and Old World wines, examining them across price ranges, vintages, regions, varietals, and blends. We find that ratings provided by Vivino users are not biased by cost; then, we study how wine characteristics, language in wine reviews, and the distribution of wine ratings can be combined to develop prediction models. More specifically, we model user behavior to develop a regression model for predicting wine ratings, and a classifier for determining user review preferences.

Following the increasing popularity of the mobile ecosystem, cybercriminals have increasingly targeted mobile ecosystems, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach.To this end, we build on MAMADROID, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AUNTIEDROID and instantiate it by using both automated (Monkey) and usergenerated inputs. We find that combining both static and dynamic analysis yields the best performance, with $F -$measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and investigate the reasons for inconsistent misclassifications across methods.

While there is an increasing flow of media stories reporting cases of cyberbullying, particularly within online social media, research efforts in the academic community are scattered over different topics across the social science and computer science academic disciplines. In this work, we explored research pertaining to cyberbullying, conducted across disciplines. We mainly sought to understand scholarly activity on intelligence techniques for the detection of cyberbullying when it occurs. Our findings suggest that the vast majority of academic contributions on cyberbullying focus on understanding the phenomenon, risk factors, and threats, with the prospect of suggesting possible protection strategies. There is less work on intelligence techniques for the detection of cyberbullying when it occurs, while currently deployed algorithms seem to detect the problem only up to some degree of success. The article summarises the current trends aiming to encourage discussion and research with a new scope; we call for more research tackling the problem by leveraging statistical models and computational mechanisms geared to detect, intervene, and prevent cyberbullying. Coupling intelligence techniques with specific web technology problems can help combat this social menace. We argue that a multidisciplinary approach is needed, with expertise from human–computer interaction, psychology, computer science, and sociology, for current challenges to be addressed and significant progress to be made.

Summary: Advances in genome sequencing and genomics research are bringing us closer to a new era of personalized medicine, where healthcare can be tailored to the individual's genetic makeup and to more effective diagnosis and treatment of rare genetic diseases. Much of this progress depends on collaborations and access to data, thus, a number of initiatives have been introduced to support seamless data sharing. Among these, the Global Alliance for Genomics and Health has developed and operates a platform, called Matchmaker Exchange (MME), which allows researchers to perform queries for rare genetic disease discovery over multiple federated databases. Queries include gene variations which are linked to rare diseases, and the ability to find other researchers that have seen or have interest in those variations is extremely valuable. Nonetheless, in some cases, researchers may be reluctant to use the platform since the queries they make (thus, what they are working on) are revealed to other researchers, and this creates concerns with respect to privacy and competitive advantage. In this paper, we present AnoniMME, a framework geared to enable anonymous queries within the MME platform. The framework, building on a cryptographic primitive called Reverse Private Information Retrieval, let researchers anonymously query the federated platform, in a multi-server setting-specifically, they write their query, along with a public encryption key, anonymously in a public database. Responses are also supported, so that other researchers can respond to queries by providing their encrypted contact details. Availability and implementation: https://github.com/bristena-op/AnoniMME.

Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models. We model the generator distribution of the training data by a mixture of k generative neural networks. These are trained together and collectively learn the generator distribution of a dataset. Data is divided into k clusters, using a novel differentially private kernel k-means, then each cluster is given to separate generative neural networks, such as Restricted Boltzmann Machines or Variational Autoencoders, which are trained only on their own cluster using differentially private gradient descent. We evaluate our approach using the MNIST dataset and a large Call Detail Records dataset, showing that it produces realistic synthetic samples, which can also be used to accurately compute arbitrary number of counting queries.

As the number and the diversity of news outlets on the Web grows, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole.

Abstract Information about people’s movements and the locations they visit enables an increasing number of mobility analytics applications, e.g., in the context of urban and transportation planning, In this setting, rather than collecting or sharing raw data, entities often use aggregation as a privacy protection mechanism, aiming to hide individual users’ location traces. Furthermore, to bound information leakage from the aggregates, they can perturb the input of the aggregation or its output to ensure that these are differentially private. In this paper, we set to evaluate the impact of releasing aggregate location time-series on the privacy of individuals contributing to the aggregation. We introduce a framework allowing us to reason about privacy against an adversary attempting to predict users’ locations or recover their mobility patterns. We formalize these attacks as inference problems, and discuss a few strategies to model the adversary’s prior knowledge based on the information she may have access to. We then use the framework to quantify the privacy loss stemming from aggregate location data, with and without the protection of differential privacy, using two real-world mobility datasets. We find that aggregates do leak information about individuals’ punctual locations and mobility profiles. The density of the observations, as well as timing, play important roles, e.g., regular patterns during peak hours are better protected than sporadic movements. Finally, our evaluation shows that both output and input perturbation offer little additional protection, unless they introduce large amounts of noise ultimately destroying the utility of the data.

Online social networks offer convenient ways to reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes (“like farms ”) has emerged alongside Facebook’s official targeted advertising platform. Nonetheless, besides a few media reports, there is little work that systematically analyzes Facebook pages’ promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users’ behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms that spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content more often, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves a precision higher than 99% and a 93% recall.

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates - i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.

As social networking takes to the mobile world, smartphone apps provide users with ever-changing ways to interact with each other. Over the past couple of years, an increasing number of apps have entered the market offering end-to-end encryption, self-destructing messages, or some degree of anonymity. However, little work thus far has examined the properties they offer. We present a taxon- omy of 18 of these apps: we first look at the features they promise in their appeal to broaden their reach and focus on 8 of the more popular ones. We present a technical evaluation, based on static and dynamic analysis, and identify a number of gaps between the claims and reality of their promises.

Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other.

This paper presents a measurement study of information leakage and SSL vulnerabilities in popular Android apps. We perform static and dynamic analysis on 100 apps, downloaded at least 10M times, that request full network access. Our experiments show that, although prior work has drawn a lot of attention to SSL implementations on mobile platforms, several popular apps (32/100) accept all certificates and all hostnames, and four actually transmit sensitive data unencrypted. We set up an experimental testbed simulating man-in-the-middle attacks and find that many apps (up to 91% when the adversary has a certificate installed on the victim's device) are vulnerable, allowing the attacker to access sensitive information, including credentials, files, personal details, and credit card numbers. Finally, we provide a few recommendations to app developers and highlight several open research problems.

Internet censorship is enforced by numerous governments worldwide, however, due to the lack of publicly available information, as well as the inherent risks of performing active measurements, it is often hard for the research community to investigate censorship practices in the wild. Thus, the leak of 600GB worth of logs from 7 Blue Coat SG-9000 proxies, deployed in Syria to filter Internet traffic at a country scale, represents a unique opportunity to provide a detailed snapshot of a real-world censorship ecosystem.

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods.

Online Social Networks (OSNs) provide users with an easy way to share content, communicate, and update others about their activities. They also play an increasingly fundamental role in coordinating and amplifying grassroots movements, as demonstrated by recent uprisings in, e.g., Egypt, Tunisia, and Turkey. At the same time, OSNs have become primary targets of tracking, profiling, as well as censorship and surveillance. In this paper, we explore the notion of undetectable communication in OSNs and introduce formal definitions, alongside system and adversarial models, that complement better understood notions of anonymity and confidentiality. We present a novel scheme for secure covert information sharing that, to the best of our knowledge, is the first to achieve undetectable communication in OSNs. We demonstrate, via an open-source prototype, that additional costs are tolerably low.

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.

The increasing penetration of Online Social Networks (OSNs) prompts the need for effectively accessing and utilizing social networking information. In numerous applications, users need to make trust and/or access control decisions involving other (possibly stranger) users, and one important factor is often the existence of common social relationships. This motivates the need for secure and privacy-preserving techniques allowing users to assess whether or not they have mutual friends.

Participatory sensing is emerging as an innovative computing paradigm that targets the ubiquity of always-connected mobile phones and their sensing capabilities. In this paper, a multitude of pioneering applications increasingly carry out pervasive collection and dissemination of information and environmental data, such as traffic conditions, pollution, temperature, and so on. Participants collect and report measurements from their mobile devices and entrust them to the cloud to be made available to applications and users. Naturally, due to the personal information associated to the reports (e.g., location, movements, etc.), a number of privacy concerns need to be considered prior to a large-scale deployment of these applications. Motivated by the need for privacy protection in participatory sensing, this paper presents a privacy-enhanced participatory sensing infrastructure. We explore realistic architectural assumptions and a minimal set of formal requirements aiming at protecting privacy of both data producers and consumers. We propose two instantiations that attain privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead.

In the last several years, micro-blogging Online Social Networks (OSNs), such as Twitter, have taken the world by storm, now boasting over 100 million subscribers. As an unparalleled stage for an enormous audience, they offer fast and reliable centralized diffusion of pithy tweets to great multitudes of information-hungry and always-connected followers. At the same time, this information gathering and dissemination paradigm prompts some important privacy concerns about relationships between tweeters, followers and interests of the latter. In this paper, we assess privacy in today's Twitter-like OSNs and describe an architecture and a trial implementation of a privacy-preserving service called Hummingbird. It is essentially a variant of Twitter that protects tweet contents, hash tags and follower interests from the (potentially) prying eyes of the centralized server. We argue that, although inherently limited by Twitter's mission of scalable information-sharing, this degree of privacy is valuable. We demonstrate, via a working prototype, that Hummingbird's additional costs are tolerably low. We also sketch out some viable enhancements that might offer better privacy in the long term.

Recent advances in DNA sequencing technologies have put ubiquitous availability of fully sequenced human genomes within reach. It is no longer hard to imagine the day when everyone will have the means to obtain and store one's own DNA sequence. Widespread and affordable availability of fully sequenced genomes immediately opens up important opportunities in a number of health-related fields. In particular, common genomic applications and tests performed in vitro today will soon be conducted computationally, using digitized genomes. New applications will be developed as genome-enabled medicine becomes increasingly preventive and personalized. However, this progress also prompts significant privacy challenges associated with potential loss, theft, or misuse of genomic data. In this paper, we begin to address genomic privacy by focusing on three important applications: Paternity Tests, Personalized Medicine, and Genetic Compatibility Tests. After carefully analyzing these applications and their privacy requirements, we propose a set of efficient techniques based on private set operations. This allows us to implement in in silico some operations that are currently performed via in vitro methods, in a secure fashion. Experimental results demonstrate that proposed techniques are both feasible and practical today.

The increasing amount of personal and sensitive information disseminated over the Internet prompts commen-surately growing privacy concerns. Digital data often lingers indefinitely and users lose its control. This motivates the desire to restrict content availability to an expiration time set by the data owner. This paper presents and formalizes the notion of Ephemeral Publishing (EphPub), to prevent the access to expired content. We propose an efficient and robust protocol that builds on the Domain Name System (DNS) and its caching mechanism. With EphPub, sensitive content is published encrypted and the key material is distributed, in a steganographic manner, to randomly selected and independent resolvers. The availability of content is then limited by the evanescence of DNS cache entries. The EphPub protocol is transparent to existing applications, and does not rely on trusted hardware, centralized servers, or user proactive actions. We analyze its robustness and show that it incurs a negligible overhead on the DNS infrastructure. We also perform a large-scale study of the caching behavior of 900K open DNS resolvers. Finally, we propose Firefox and Thunderbird extensions that provide ephemeral publishing capabilities, as well as a command-line tool to create ephemeral files.

The scope of mobile phones has skyrocketed in recent years to such an extent that smartphone sales are expected to surpass those of PCs by the end of 2011. Equipped with relatively powerful processors and fairly large memory and storage capabilities, smartphones can accommodate increasingly complex interactive applications. As a result, the growing amount of sensitive information shared by smartphone users raises serious privacy concerns and motivates the need for appropriate privacy-preserving mechanisms. In this paper, we present a novel architecture geared for privacy-sensitive applications where personal information is shared among users and decisions are made based on given optimization criteria. Specifically, we focus on two application scenarios: (i) privacy-preserving interest sharing, i.e., discovering shared interests without leaking users' private information, and (ii) private scheduling, i.e., determining common availabilities and location preferences that minimize associate costs, without exposing any sensitive information. We propose efficient yet provably-private solutions, and conduct an extensive experimental analysis that attests to the practicality of the attained privacy features.

Participatory Sensing combines the ubiquity of mobile phones with the sensing capabilities of Wireless Sensor Networks. It targets the pervasive collection of information, e.g., temperature, traffic conditions, or medical data. Users produce measurements from their mobile devices, thus, a number of privacy concerns -- due to the personal information conveyed by reports -- may hinder the large-scale deployment of participatory sensing applications. Prior work has attempted to protect privacy in participatory sensing, but it relied on unrealistic assumptions and achieved no provably-secure guarantees. In this paper, we introduce PEPSI: Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of formal requirements aiming at protecting privacy of both data producers and consumers. We also present an instantiation that attains privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead. Finally, we highlight some problems that call for further research in this developing area.

Wireless sensor networks (WSNs) provide sensing and monitoring services by means of many tiny autonomous devices equipped with wireless radio transceivers. With large-scale WSNs being deployed on a long-term basis, not only security but also privacy issues must be taken into account. Furthermore, when network operators offer on-demand access to sensor measurements to their clients, query mechanisms should ideally leak neither client interests nor query patterns. In this paper, we present a privacy-preserving WSN query mechanism that uses standard cryptographic techniques. Besides preventing unauthorized entities from accessing sensor readings, it minimizes leakage of (potentially sensitive) information about users' query targets and patterns.

This work introduces FAIR, a novel framework for Fuzzy-based Aggregation providing In-network Resilience for Wireless Sensor Networks (WSN). FAIR addresses the possibility of malicious aggregator nodes manipulating data. It provides data-integrity based on a trust level of the WSN response and it tolerates link or node failures. Compared to available solutions, it offers a general aggregation model and makes the trust level visible to the querier. We classify the proposed approach as complementary to protocols ensuring resilience against sensor leaf nodes providing faulty data. Thanks to our flexible resilient framework and due to the use of Fuzzy Inference Schemes, we achieve promising results within a short design cycle.

Wireless sensor networks are emerging as a practical and powerful solution to monitoring problems. However, their use in real world scenarios faces several issues. In fact, sensor nodes are tiny devices with very limited resources, and thus it is important to optimize the battery consumption. For instance, aggregation operations are widely used to minimize transmission operations, that are the most power-consuming operations for the devices. In addition, since sensor nodes operate in open and often hostile environments, security issues must be taken into account. In this paper, we propose a novel aggregation scheme which provides data integrity and privacy-protection and guarantees efficiency with respect to data transmissions, congestion, and memory overhead. To test performance of the scheme, we implemented it within a simulation software environment. We tested its performance to verify its feasibility and we evaluated the computation and power required to ensure security. Simulation results confirmed that our scheme is to be considered efficient enough to being used in real world scenarios.

We present an experiment relative to the use of Bluetooth wireless technology to provide network support for midlet applications accessing Web services. We refer to the most common architecture used to invoke Web services, where a client and a server exchange SOAP messages using HTTP as the transport protocol. To the best of our knowledge, there is no implemented support for executing a HTTP POST operation over a Bluetooth channel. Therefore, to guarantee the independence of the application from the type of communication channel used, in this paper, we deal with the problem of designing a framework allowing a Java application programmer to directly interface Web services from a mobile device using a Bluetooth connection. This paper presents a proof of concept of how Bluetooth technology can be used to design, develop, and deploy Web services-based applications. According to our experiments, programming interfaces like Blue Cove and kSOAP, despite being still under development, are mature enough to be used as the underlying technologies for Web services invocation over Bluetooth in a real world application