Splitting Hairs and Network Traces

Beckerle, Matthias; Magnusson, Jonathan; Pulls, Tobias

doi:10.1145/3559613.3563199

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The UI mock-ups were designed according to the use case scenario described in Section 2.2 and based on earlier designs in the project PAPAYA [8]. In Figure 2, we present three screens from the UI mock-ups 1 .…”

Section: Ui Mock-up Walk-throughsmentioning

confidence: 99%

Structural and functional explanations for informing lay and expert users: the case of functional encryption

Alaqra

Karegar

Fischer‐Hübner

2023

PoPETs

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Usable explanations of privacy-enhancing technologies (PETs) help users make more informed privacy decisions, but the explanations of PETs are generally geared toward individuals with more technical knowledge. To explain functional encryption (FE) to experts and laypersons, we investigate structural and functional explanations and explore users' interests and preferences, as well as how they affect users' comprehension and decisions about sharing data. To this end (with an EU-based population), we conducted four focus groups, in combination with walk-throughs, with 13 participants in the first study, followed by an online survey with 347 experts and 370 laypersons. Both explanations were considered useful in fulfilling the different needs of participants interested in the privacy policy information. Participants, regardless of their expertise, trusted and were more satisfied with the structural explanation. However, functional explanations had a higher contribution to all participants' comprehension. We, therefore, recommend combining both types of explanations for a usable privacy policy.

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Section: Ui Mock-up Walk-throughsmentioning

confidence: 99%

Structural and functional explanations for informing lay and expert users: the case of functional encryption

Alaqra

Karegar

Fischer‐Hübner

2023

PoPETs

View full text Add to dashboard Cite

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“…The split-based solutions do achieve subflow unlinkability to some extent. However, a recent study from Beckerle et al [6] performs a critical evaluation of splitting-based defenses, and demonstrates that the defenses are less effective once the adversary is able to observe the vast majority of traffic. Beckerle et al [6] develop novel Maturesc attacks against existing splittingbased defenses that can achieve good accuracy.…”

Section: Related Workmentioning

confidence: 99%

RAVEN: Stateless Rapid IP Address Variation for Enterprise Networks

Wang

Kim

Mittal

et al. 2023

PoPETs

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Enterprise networks face increasing threats against the privacy of their clients. Existing enterprise services like Network Address Translation (NAT) offer limited privacy protection, at the cost of requiring per-flow state. In this paper, we introduce RAVEN (Rapid Address Variation for Enterprise Networks), a network-based privacy solution that is complementary to application-layer defenses. RAVEN protects privacy by frequently changing the client's public IP address. With RAVEN, a client is not limited to using a single IP address at a given time, or even for a given connection. RAVEN goes further, breaking the association between packets that belong to the same connection by frequently changing the client's IP address within a single connection. RAVEN achieves this through a novel division of labor: the client uses a transport protocol, like QUIC, that supports seamless connection migration, and decides when to switch its IP address, while the enterprise network actually changes the client's IP address in a stateless manner at line rate and ensures end-to-end packet delivery. We implement RAVEN using QUIC and off-the-shelf programmable switches. We deploy RAVEN in a test IPv6 network and evaluate its defense against webpage fingerprinting attacks. Even with a strong adversary, the average precision of the best adaptive attacks drops from 0.96 to 0.84, with a 0.5% degradation in client throughput. When RAVEN changes IP addresses at unpredictable frequency, the precision of the best attacks falls to 0.78---the same effectiveness as WTF-PAD.

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