As reliance on online video continues to increase throughout all facets of society, it is critical to address the security and privacy threat of video fingerprinting, in which a local, passive adversary monitors a victim’s encrypted connection to a video server to infer which videos they are watching. These attacks attain high accuracy in realistic scenarios, while defenses that offer an acceptable trade-off between protection, overhead, and user experience are lacking. In this paper, we motivate application-layer defenses against video fingerprinting and present Dodge, a client-side framework for application-layer video fingerprinting defenses, implemented as a fork of the dash.js video player. Dodge provides the infrastructure and building blocks for defenses as well as a plug-and-play interface, making defense development and use straightforward while still providing maximal control over video flows. As a proof of concept, we use Dodge to implement a mimicry defense and show through live deployments that Dodge and the defense operate seamlessly, with modest overhead and very low user experience impact, while reducing attacker success close to theoretical bounds. Dodge can easily be deployed at scale and in a number of scenarios, with no changes to servers or network components; our analyses also lead to a host of insights that we hope will aid in deployment efforts.

Cryptocurrencies’ pseudonymity property poses regulatory challenges and has attracted illicit actors that try to avoid oversight. To counter this, the U.S. Treasury’s Office of Foreign Assets Control (OFAC) sanctions individuals and entities using Bitcoin for cybercrime, terrorism financing, and other illicit activities. However, the effectiveness of these measures remains uncertain. This study analyzes over 13 million Bitcoin transactions linked to sanctioned entities, tracing fund flows and exchange interactions. We find that ~175,000 BTC was moved before sanctions took effect, with only 50 BTC remaining post-sanction, indicating preemptive fund displacement. Cybercrime-linked addresses accounted for the largest transfers—sometimes exceeding $1 billion—while sanctioned entities favored large, direct transactions to exchanges. Despite activity dropping immediately after sanctions took effect, some entities continued transacting for up to 1,500 days, exposing enforcement gaps. Our findings highlight key challenges in sanction enforcement, including delayed restrictions, exchange compliance gaps, and strategic fund movements. These insights inform policymakers and regulators seeking to strengthen cryptocurrency financial controls.

The rapid expansion of cloud computing has transformed data storage and processing by providing unprecedented convenience and scalability. However, this progress is shadowed by significant data security challenges, primarily as users must rely on cloud service providers to enforce robust security protocols. Homomorphic encryption (HE) offers a potential solution by allowing computations on encrypted data, thereby maintaining confidentiality without compromising functionality. However, HE can be computationally intensive, raising concerns about its practicality in real-world applications, particularly when dealing with large files. Moreover, constantly re-encrypting an entire file after every modification is inefficient and introduces substantial performance overheads. While traditional delta encoding can be used to optimize bandwidth by transmitting only file modifications, it faces security and privacy concerns as the server must access unencrypted file contents. In this paper, we address these challenges by introducing a novel delta encoding scheme tailored for seamless compatibility with HE, enhancing data confidentiality while maintaining efficiency. Our approach minimizes the overhead of re-encryption and improves performance by encrypting and transmitting only the modified file parts. We evaluate the performance of our scheme across various parameters and test cases, comparing it to a state-of-the-art delta encoding approach. Our findings demonstrate many similarities while highlighting the tradeoffs of our HE-enabling solution. Additionally, we explore the performance impacts of integrating HE with our delta encoding scheme and provide insights into the practical constraints and requirements for real-world deployment in cloud computing environments.

Despite the clear dominance of video streaming traffic on the Internet and the significant ramifications of disclosure of which videos users are streaming, video fingerprinting has received relatively little attention compared to other traffic analysis domains. Existing attacks are tailored to undefended traffic and mostly rely on a few manually crafted features. Meanwhile, potential defenses are ad hoc, often impractical, and typically only mentioned briefly. Drawing from progress made on website fingerprinting, we aim to improve current standards for attacks and defenses for video streaming traffic while highlighting a critical and underexplored issue on today's Internet. We show that directional and timing-based attacks that leverage CNNs are competitive with state-of-the-art video fingerprinting attacks, in many cases with far less training data. We also provide the first extensive study of potential defenses, which considers performance against attacks, overheads, and user QoE; and we present a novel defense design that boasts both broader applicability and greater efficacy than existing proposals.

The underlying trading mechanisms of electronic securities exchanges have mostly stayed the same over the years with some additions and improvements. However, over the recent decade, high-frequency traders using algorithmic trading have shifted the field using practices that many consider unfair or unethical. In addition, insider trading continues to cause trust issues on certain trading platforms. In this paper, we present PET-Exchange, a privacy-preserving framework for trading securities on an electronic stock exchange. By using homomorphic encryption, PET-Exchange prevents information disclosures and unfair advantages in the trading processes. By matching and trading encrypted orders, we study the performance under various volumes and timing constraints, and compare this to the unencrypted counterparts. Our analysis of PET-Exchange using market trade data shows the privacy and cryptographic tradeoffs, demonstrating it to be suitable for small-scale trading and privacy-preserving auctions. Finally, we discuss the potential impact on transparency, fairness, and opportunities for financial crime in an electronic securities exchange. The insights we provide take us one step closer to a privacy-aware and fair public securities exchange.

Dual Connectivity (DC) is an important lower-layer feature accelerating the transition from 4G to 5G that also is expected to play an important role in standalone 5G radio networks. However, even though the packet reordering introduced by DC can significantly impact the performance of upper-layer protocols, no prior work has studied the impact of DC on QUIC. In this paper, we present the first such performance study. Using a series of throughput and fairness experiments, we show how QUIC is affected by different DC parameters, network conditions, and whether the DC implementation aims to improve throughput or reliability. Results for two QUIC implementations (aioquic, ngtcp2) and two congestion control algorithms (NewReno, CUBIC) are presented under both static and highly time-varying network conditions Our findings provide network operators with insights and understanding into the impacts of splitting QUIC traffic in a DC environment. With reasonably selected DC parameters and increased UDP receive buffers, QUIC over DC performs similarly to TCP over DC and achieves optimal fairness under symmetric link conditions when DC is not used for packet duplication. The insights can help network operators provide modern users with better end-to-end service when deploying DC.