Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2, 595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer’s daily deposits by more than .

Despite active privacy research on sophisticated web tracking techniques (e.g., fingerprinting, cache collusion, bounce tracking, CNAME cloaking), most tracking on the web is basic “stateful” tracking enabled by classical browser storage policies sharing per-site storage across all HTTP contexts. Alternative, privacy-preserving storage policies, especially for third-party contexts, have been proposed and even deployed, but these can break websites that presume traditional, non-partitioned storage. Such breakage discourages privacy-preserving experimentation, cementing the dismal status quo. Our work measures the privacy vs. compatibility trade-offs of representative third-party storage policies to enable design of browsers that are both compatible and privacy respecting. Our contributions include web-scale measurements of page behaviors under multiple third-party storage policies inspired by production browsers. We define metrics for measuring aggregate effects on web privacy and compatibility, including a novel system for quantitatively estimating aggregate website breakage under different policies. We find that making third-party storage partitioned by first-party, and lifetimes by site-session achieves the best privacy and compatibility trade-off. We provide complete measurement datasets and storage policy implementations.

Recommender systems are commonly trained on centrally-collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender’s collection and handling of potentially sensitive data. Several privacy-aware recommender systems have been proposed in recent literature, but comparatively little attention has been given to systems at the intersection of implicit feedback and privacy. To address this shortcoming, we propose a practical federated recommender system for implicit data under user-level local differential privacy (LDP). The privacy-utility trade-off is controlled by parameters ϵ and k, regulating the per-update privacy budget and the number of ϵ-LDP gradient updates sent by each user, respectively. To further protect the user’s privacy, we introduce a proxy network to reduce the fingerprinting surface by anonymizing and shuffling the reports before forwarding them to the recommender. We empirically demonstrate the effectiveness of our framework on the MovieLens dataset, achieving up to Hit Ratio with K=10 ([email protected]) 0.68 on 50,000 users with 5,000 items. Even on the full dataset, we show that it is possible to achieve reasonable utility with [email protected]>0.5 without compromising user privacy.

Accurate web measurement is critical for understanding and improving security and privacy online. Such measurements implicitly assume that automated crawls generalize to typical web user experience. But anecdotal evidence suggests the web behaves differently when seen via well-known measurement endpoints or measurement automation frameworks, for various reasons. Our work improves the state of web privacy and security by investigating how key measurements differ when using naive crawling tool defaults vs. careful attempts to match “real” users across the Tranco top 25k web domains. We find web privacy and security measurements significantly affected by vantage point and browser configuration. We conclude that unless researchers ensure their web measurement tools match real world user experience, the research community is likely missing important signals systematically. For example, we find browser configuration alone causing shifts in 19% of known ad and tracking domains encountered and altering the loading frequency of up to 10% of distinct JavaScript code units executed. We find network vantage point having similar, though less dramatic, effects on the same web metrics. To ensure reproducibility, we carefully document our methodology and publish both our code and collected data.

The median webpage has increased in size by more than 80% in the last 4 years. This extra complexity allows for a rich browsing experience, but it hurts the majority of mobile users which still pay for their traffic. This has motivated several data-saving solutions, which aim at reducing the complexity of webpages by transforming their content. Despite each method being unique, they either reduce user privacy by further centralizing web traffic through data-saving middleboxes or introduce web compatibility (Web-compat) issues by removing content that breaks pages in unpredictable ways.

Funding the production of quality online content is a pressing problem for content producers. The most common funding method, online advertising, is rife with well-known performance and privacy harms, and an intractable subject-agent conflict: many users do not want to see advertisements, depriving the site of needed funding.

Filter lists play a large and growing role in protecting and assisting web users. The vast majority of popular filter lists are crowd-sourced, where a large number of people manually label resources related to undesirable web resources (e.g. ads, trackers, paywall libraries), so that they can be blocked by browsers and extensions.

Most popular web browsers include “reader modes” that improve the user experience by removing un-useful page elements. Reader modes reformat the page to hide elements that are not related to the page's main content. Such page elements include site navigation, advertising related videos and images, and most JavaScript. The intended end result is that users can enjoy the content they are interested in, without distraction.

Researchers have shown that, in recent years, unwanted web tracking is on the rise, with browser-based fingerprinting being adopted by more and more websites as a viable alternative to third-party cookies. In this paper we propose PriVaricator, a solution to the problem of browser-based fingerprinting. A key insight is that when it comes to web tracking, the real problem with fingerprinting is not uniqueness of a fingerprint, it is linkability, i.e., the ability to connect the same fingerprint across multiple visits. Thus, making fingerprints non-deterministic also makes them hard to link across browsing sessions. In PriVaricator we use the power of randomization to "break" linkability by exploring a space of parameterized randomization policies. We evaluate our techniques in terms of being able to prevent fingerprinting and not breaking existing (benign) sites. The best of our randomization policies renders all the fingerprinters we tested ineffective, while causing minimal damage on a set of 1000 Alexa sites on which we tested, with no noticeable performance overhead.

Crowd-sourcing is increasingly being used for providing responses to polls and surveys on a large scale. Companies such as SurveyMonkey and Instant.ly are attempting to make crowd-sourced surveys commonplace, by making it easy to pose survey questions using an easy-to-use UI and retrieve results with a relatively low latency by having dedicated crowds at their disposal. In this paper we argue that the ease with which polls can be created conceals an inherent difficulty: the survey maker does not know how many workers to hire for their survey. Asking too few may lead to samples sizes that `"do not look impressive enough." Asking too many clearly involves spending extra money, which can quickly become costly. Existing crowd-sourcing platforms do not provide help with this, neither, one can argue, do they have any incentive to do so. We present a systematic approach to determining how many samples (i.e. workers) are required to achieve a certain level of statistical significance by showing how to automatically perform power analysis on questions of interest. Using a range of queries we demonstrate that power analysis can save significant amounts of money and time by concluding that frequently, only a handful of results is required to arrive at a certain decision. We have implemented our approach within InterPoll, aprogrammable developer-driven polling system that uses a generic crowd (Mechanical Turk) as a back-end. Power analysis is automatically performed given both the structure of the query and the data that is being polled from the crowd. In all of our studies we are able to obtain statistically significant answers for under $30, with most costing less than $10. Our approach saves both time and money for the survey maker.

The rise of the software-as-a-service paradigm has led to the development of a new breed of sophisticated, interactive applications often called Web 2.0. While Web applications have become larger and more complex, Web application developers today have little visibility into the end-to-end behavior of their systems. This article presents AjaxScope, a dynamic instrumentation platform that enables cross-user monitoring and just-in-time control of Web application behavior on end-user desktops. AjaxScope is a proxy that performs on-the-fly parsing and instrumentation of JavaScript code as it is sent to users’ browsers. AjaxScope provides facilities for distributed and adaptive instrumentation in order to reduce the client-side overhead, while giving fine-grained visibility into the code-level behavior of Web applications. We present a variety of policies demonstrating the power of AjaxScope, ranging from simple error reporting and performance profiling to more complex memory leak detection and optimization analyses. We also apply our prototype to analyze the behavior of over 90 Web 2.0 applications and sites that use significant amounts of JavaScript.

Program analysis has been increasingly used in software engineering tasks such as auditing programs for security vulnerabilities and finding errors in general. Such tools often require analyses much more sophisticated than those traditionally used in compiler optimizations. In particular, context-sensitive pointer alias information is a prerequisite for any sound and precise analysis that reasons about uses of heap objects in a program. Context-sensitive analysis is challenging because there are over 1014 contexts in a typical large program, even after recursive cycles are collapsed. Moreover, pointers cannot be resolved in general without analyzing the entire program.