Online financial scams represent a long-standing and serious threat for which people seek help. We present a study to understand people’s in situ motivations for engaging with scams and the help needs they express before, during, and after encountering a scam. We identify the main emotions scammers exploited (e.g., fear, hope) and characterize how they did so. We examine factors—such as financial insecurity and legal precarity—which elevate people’s risk of engaging with specific scams and experiencing harm. We indicate when people sought help and describe their help-seeking needs and emotions at different stages of the scam. We discuss how these needs could be met through the design of contextually-specific prevention, diagnostic, mitigation, and recovery interventions.

Zero-knowledge proofs of training (zkPoT) allow a party to prove that a model is trained correctly on a committed dataset without revealing any additional information about the model or the dataset. Existing zkPoT protocols prove the entire training process in zero knowledge; i.e., they prove that the final model was obtained in an iterative fashion starting from the training data and a random seed (and potentially other parameters) and applying the correct algorithm at each iteration. This approach inherently requires the prover to perform work linear to the number of iterations.

We study the ability of language models to reason about appropriate information disclosure - a central aspect of the evolving field of agentic privacy. Whereas previous works have focused on evaluating a model's ability to align with human decisions, we examine the role of ambiguity and missing context on model performance when making information-sharing decisions. We identify context ambiguity as a crucial barrier for high performance in privacy assessments. By designing Camber, a framework for context disambiguation, we show that model-generated decision rationales can reveal ambiguities and that systematically disambiguating context based on these rationales leads to significant accuracy improvements (up to 13.3% in precision and up to 22.3% in recall) as well as reductions in prompt sensitivity. Overall, our results indicate that approaches for context disambiguation are a promising way forward to enhance agentic privacy reasoning.

In this paper, we explore the feasibility of leveraging large language models (LLMs) to automate or otherwise assist human raters with identifying harmful content including hate speech, harassment, violent extremism, and election misinformation. Using a dataset of 50,000 user comments, we demonstrate that LLMs can achieve 90 % accuracy when compared to human verdicts. We explore how to best leverage these capabilities, proposing five design patterns that integrate LLMs with human rating, such as pre-filtering non-violative content, detecting potential errors in human rating, or surfacing critical context to support human rating. We outline how to support all of these design patterns using a single, optimized prompt. Beyond these synthetic experiments, we share how piloting our proposed techniques in a real-world review queue yielded a 41.5% improvement in optimizing available human rater capacity, and a 9–11 % increase (absolute) in precision and recall for detecting violative content.

The Internet's combination of low communication cost, global reach, and functional anonymity has allowed fraudulent scam volumes to reach new heights. Designing effective interventions requires first understanding the context: how scammers reach potential victims, the earnings they make, and any potential bottlenecks for durable interventions. In this short paper, we focus on these questions in the context of cryptocurrency giveaway scams, where victims are tricked into irreversibly transferring funds to scammers under the pretense of even greater returns. Combining data from Twitter (also known as X), YouTube and Twitch livestreams, landing pages, and cryptocurrency blockchains, we measure how giveaway scams operate at scale. We find that 1 in 1000 scam tweets, and 4 in 100,000 livestream views, net a victim, and that scammers managed to extract nearly $4.62 million from just hundreds of victims during our measurement window.

The Web public key infrastructure is essential to providing secure communication on the Internet today, and certificate authorities play a crucial role in this ecosystem by issuing certificates. These authorities may misissue certificates or suffer misuse attacks, however, which has given rise to the Certificate Transparency (CT) project. The goal of CT is to store all issued certificates in public logs, which can then be checked for the presence of potentially misissued certificates. Thus, the requirement that a given certificate is indeed in one (or several) of these logs lies at the core of CT. In its current deployment, however, most individual clients do not check that the certificates they see are in logs, as requesting a proof of inclusion directly reveals the certificate and thus creates the clear potential for a violation of that client’s privacy. In this paper, we explore the techniques that have been proposed for privacy-preserving auditing of certificate inclusion, focusing on their effectiveness, efficiency, and suitability in a near-term deployment. In doing so, we also explore the parallels with related problems involving browser clients. Guided by a set of constraints that we develop, we ultimately observe several key limitations in many proposals, ranging from their privacy provisions to the fact that they focus on the interaction between a client and a log but leave open the question of how a client could privately report any certificates that are missing.

Information technology is increasingly designed to increase information transparency as a way to increase trust. However, it can be hard to comprehend and anticipate the social implications of information visibility, people’s competing expectations of transparency versus privacy, and the role of enabling technologies. We devised Live Action Role Play (LARP) as a means for people to explore the potential and consequences of transparency, within a social context of daily transactions related to food, fashion, and finance. Through the process of deliberation, we enabled participants to critically assess their co-created LARP experience and articulate their transparency expectations for design considerations. We report on insights into their expectations and perceived limitations of information technology in delivering transparency, including social measures required to realise the full potential of technology, as well as transparency, while minimising unintended consequences.

We give a protocol for Asynchronous Distributed Key Generation (A-DKG) that is optimally resilient (can withstand f < n over 3 faulty parties), has a constant expected number of rounds, has Õ (n3) expected communication complexity, and assumes only the existence of a PKI. Prior to our work, the best A-DKG protocols required Ω(n) expected number of rounds, and Ω(n4) expected communication.

We argue that existing security, privacy, and antiabuse protections fail to address the growing threat of online hate and harassment. In order for our community to understand and address this gap, we propose a taxonomy for reasoning about online hate and harassment. Our taxonomy draws on over 150 interdisciplinary research papers that cover disparate threats ranging from intimate partner violence to coordinated mobs. In the process, we identify seven classes of attacks—such as toxic content and surveillance—that each stem from different attacker capabilities and intents. We also provide longitudinal evidence from a three-year survey that hate and harassment is a pervasive, growing experience for online users, particularly for at-risk communities like young adults and people who identify as LGBTQ+. Responding to each class of hate and harassment requires a unique strategy and we highlight five such potential research directions that ultimately empower individuals, communities, and platforms to do so.

Token-curated registries (TCRs) are a mechanism by which a set of users are able to jointly curate a reputable list about real-world information. Entries in the registry may have any form, so this primitive has been proposed for use—and deployed—in a variety of decentralized applications, ranging from the simple joint creation of lists to helping to prevent the spread of misinformation online. Despite this interest, the security of this primitive is not well understood, and indeed existing constructions do not achieve strong or provable notions of security or privacy. In this paper, we provide a formal cryptographic treatment of TCRs as well as a construction that provably hides the votes cast by individual curators. Along the way, we provide a model and proof of security for an underlying voting scheme, which may be of independent interest. We also demonstrate, via an implementation and evaluation, that our construction is practical enough to be deployed even on a constrained decentralized platform like Ethereum.

The core technical component of blockchains is consensus: how to reach agreement among a distributed network of nodes. A plethora of blockchain consensus protocols have been proposed---ranging from new designs, to novel modifications and extensions of consensus protocols from the classical distributed systems literature. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematization of knowledge of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work; (ii) proof-of-X protocols that replace proof-of-work with more energy-efficient alternatives; and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. This survey is guided by a systematization framework we develop, to highlight the various building blocks of blockchain consensus design, along with a discussion on their security and performance properties. We identify research gaps and insights for the community to consider in future research endeavours.

State channels are a leading approach for improving the scalability of blockchains and cryptocurrencies. They allow a group of distrustful parties to optimistically execute an application-defined program amongst themselves, while the blockchain serves as a backstop in case of a dispute or abort. This effectively bypasses the congestion, fees and performance constraints of the underlying blockchain in the typical case. However, state channels introduce a new and undesirable assumption that a party must remain online and synchronised with the blockchain at all times to defend against execution fork attacks. An execution fork can revert a state channel's history, potentially causing financial damage to a party that is innocent except for having crashed. To provide security even to parties that may go offline for an extended period of time, we present Pisa, the first protocol to propose an accountable third party who can be hired by parties to cancel execution forks on their behalf. To evaluate Pisa, we provide a proof-of-concept implementation for a simplified Sprites and we demonstrate that it is cost-efficient to deploy on the Ethereum network.

Ever since their introduction, zero-knowledge proofs have become an important tool for addressing privacy and scalability concerns in a variety of applications. In many systems each client downloads and verifies every new proof, and so proofs must be small and cheap to verify. The most practical schemes require either a trusted setup, as in (pre-processing) zk-SNARKs, or verification complexity that scales linearly with the complexity of the relation, as in Bulletproofs. The structured reference strings required by most zkSNARK schemes can be constructed with multi-party computation protocols, but the resulting parameters are specific to an individual relation. Groth et al. discovered a zk-SNARK protocol with a universal structured reference string that is also updatable, but the string scales quadratically in the size of the supported relations. Here we describe a zero-knowledge SNARK, Sonic, which supports a universal and continually updatable structured reference string that scales linearly in size. We also describe a generally useful technique in which untrusted “helpers” can compute advice that allows batches of proofs to be verified more efficiently. Sonic proofs are constant size, and in the “helped” batch verification context the marginal cost of verification is comparable with the most efficient SNARKs in the literature

Abstract Cryptocurrencies allow users to securely transfer money without relying on a trusted intermediary, and the transparency of their underlying ledgers also enables public verifiability. This openness, however, comes at a cost to privacy, as even though the pseudonyms users go by are not linked to their real-world identities, all movement of money among these pseudonyms is traceable. In this paper, we present Möbius, an Ethereum-based tumbler or mixing service . Möbius achieves strong notions of anonymity, as even malicious senders cannot identify which pseudonyms belong to the recipients to whom they sent money, and is able to resist denial-of-service attacks. It also achieves a much lower off-chain communication complexity than all existing tumblers, with senders and recipients needing to send only two initial messages in order to engage in an arbitrary number of transactions.

In this paper, we initiate a formal study of transparency, which in recent years has become an increasingly critical requirement for the systems in which people place trust. We present the abstract concept of a transparency overlay, which can be used in conjunction with any system to give it provable transparency guarantees, and then apply the overlay to two settings: Certificate Transparency and Bitcoin. In the latter setting, we show that the usage of our transparency overlay eliminates the need to engage in mining and allows users to store a single small value rather than the entire blockchain. Our transparency overlay is generically constructed from a signature scheme and a new primitive we call a dynamic list commitment, which in practice can be instantiated using a collision-resistant hash function.

We consider the problem of constructing anonymous credentials for use in a setting where the issuer of credentials is also the verifier, or more generally where the issuer and verifier have a shared key. In this setting we can use message authentication codes (MACs) instead of public key signatures as the basis for the credential system.

A signature scheme is malleable if, on input a message and a signature, it is possible to efficiently compute a signature on a related message, for a transformation that is allowed with respect to this signature scheme. In this paper, we first provide new definitions for malleable signatures that allow us to capture a broader range of transformations than was previously possible. We then give a generic construction based on malleable zero-knowledge proofs that allows us to construct malleable signatures for a wide range of transformation classes, with security properties that are stronger than those that have been achieved previously. Finally, we construct delegatable anonymous credentials from signatures that are malleable with respect to an appropriate class of transformations (that we show our malleable signature supports). The resulting instantiation satisfies a stronger security notion than previous schemes while also scaling linearly with the number of delegations.

Modern embedded computing systems such as medical devices, airplanes, and automobiles continue to dominate some of the most critical aspects of our lives. In such systems, the movement of information throughout a device must be tightly controlled to prevent violations of privacy or integrity. Unfortunately, bounding the flow of information can often present a significant challenge, as information can flow through channels that are difficult to detect, such as timing channels. As has been demonstrated by recent research in hardware security, information flow tracking techniques deployed at the hardware or gate level show promise at identifying these “timing flows” but provide no formal statements about this claim NOR mechanisms for separating out timing information from other types of flows. In this paper, we first prove that gate-level information flow tracking can in fact detect timing flows. In addition, we work to identify these timing flows separately from other flows by presenting a framework for identifying a different type of flow that we call functional flows. By using this framework to either confirm or rule out the existence of such flows, we leverage the previous work in hardware information flow tracking to effectively isolate timing flows. To show the effectiveness of this model, we demonstrate its usage on three practical examples: a shared bus (I2C), a cache in a MIPS-based processor, and an RSA encryption core, all of which were written in Verilog/VHDL and then simulated in a variety of scenarios. In each scenario, we demonstrate how our framework can be used to identify timing and functional flows and also analyze our model's overhead.

This work identifies a new formal basis for hardware information flow security by providing a method to separate timing flows from other flows of information. By developing a framework for identifying these different classes of information flow at the gate-level, one can either confirm or rule out the existence of such flows in a provable manner. To demonstrate the effectiveness of our presented model, we discuss its usage on a practical example: a CPU cache in a MIPS processor written in Verilog HDL and simulated in a scenario which accurately models previous cache-timing attacks. We demonstrate how our framework can be used to isolate the timing channel used in these attacks.

Bitcoin is a purely online virtual currency, unbacked by either physical commodities or sovereign obligation; instead, it relies on a combination of cryptographic protection and a peer-to-peer protocol for witnessing settlements. Consequently, Bitcoin has the unintuitive property that while the ownership of money is implicitly anonymous, its flow is globally visible. In this paper we explore this unique characteristic further, using heuristic clustering to group Bitcoin wallets based on evidence of shared authority, and then using re-identification attacks (i.e., empirical purchasing of goods and services) to classify the operators of those clusters. From this analysis, we characterize longitudinal changes in the Bitcoin market, the stresses these changes are placing on the system, and the challenges for those seeking to use Bitcoin for criminal or fraudulent purposes at scale.