5,692 research outputs found
Dovetail: Stronger Anonymity in Next-Generation Internet Routing
Current low-latency anonymity systems use complex overlay networks to conceal
a user's IP address, introducing significant latency and network efficiency
penalties compared to normal Internet usage. Rather than obfuscating network
identity through higher level protocols, we propose a more direct solution: a
routing protocol that allows communication without exposing network identity,
providing a strong foundation for Internet privacy, while allowing identity to
be defined in those higher level protocols where it adds value.
Given current research initiatives advocating "clean slate" Internet designs,
an opportunity exists to design an internetwork layer routing protocol that
decouples identity from network location and thereby simplifies the anonymity
problem. Recently, Hsiao et al. proposed such a protocol (LAP), but it does not
protect the user against a local eavesdropper or an untrusted ISP, which will
not be acceptable for many users. Thus, we propose Dovetail, a next-generation
Internet routing protocol that provides anonymity against an active attacker
located at any single point within the network, including the user's ISP. A
major design challenge is to provide this protection without including an
application-layer proxy in data transmission. We address this challenge in path
construction by using a matchmaker node (an end host) to overlap two path
segments at a dovetail node (a router). The dovetail then trims away part of
the path so that data transmission bypasses the matchmaker. Additional design
features include the choice of many different paths through the network and the
joining of path segments without requiring a trusted third party. We develop a
systematic mechanism to measure the topological anonymity of our designs, and
we demonstrate the privacy and efficiency of our proposal by simulation, using
a model of the complete Internet at the AS-level
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
HORNET: High-speed Onion Routing at the Network Layer
We present HORNET, a system that enables high-speed end-to-end anonymous
channels by leveraging next generation network architectures. HORNET is
designed as a low-latency onion routing system that operates at the network
layer thus enabling a wide range of applications. Our system uses only
symmetric cryptography for data forwarding yet requires no per-flow state on
intermediate nodes. This design enables HORNET nodes to process anonymous
traffic at over 93 Gb/s. HORNET can also scale as required, adding minimal
processing overhead per additional anonymous channel. We discuss design and
implementation details, as well as a performance and security evaluation.Comment: 14 pages, 5 figure
Evaluation of Anonymized ONS Queries
Electronic Product Code (EPC) is the basis of a pervasive infrastructure for
the automatic identification of objects on supply chain applications (e.g.,
pharmaceutical or military applications). This infrastructure relies on the use
of the (1) Radio Frequency Identification (RFID) technology to tag objects in
motion and (2) distributed services providing information about objects via the
Internet. A lookup service, called the Object Name Service (ONS) and based on
the use of the Domain Name System (DNS), can be publicly accessed by EPC
applications looking for information associated with tagged objects. Privacy
issues may affect corporate infrastructures based on EPC technologies if their
lookup service is not properly protected. A possible solution to mitigate these
issues is the use of online anonymity. We present an evaluation experiment that
compares the of use of Tor (The second generation Onion Router) on a global
ONS/DNS setup, with respect to benefits, limitations, and latency.Comment: 14 page
Adaptive Traffic Fingerprinting for Darknet Threat Intelligence
Darknet technology such as Tor has been used by various threat actors for
organising illegal activities and data exfiltration. As such, there is a case
for organisations to block such traffic, or to try and identify when it is used
and for what purposes. However, anonymity in cyberspace has always been a
domain of conflicting interests. While it gives enough power to nefarious
actors to masquerade their illegal activities, it is also the cornerstone to
facilitate freedom of speech and privacy. We present a proof of concept for a
novel algorithm that could form the fundamental pillar of a darknet-capable
Cyber Threat Intelligence platform. The solution can reduce anonymity of users
of Tor, and considers the existing visibility of network traffic before
optionally initiating targeted or widespread BGP interception. In combination
with server HTTP response manipulation, the algorithm attempts to reduce the
candidate data set to eliminate client-side traffic that is most unlikely to be
responsible for server-side connections of interest. Our test results show that
MITM manipulated server responses lead to expected changes received by the Tor
client. Using simulation data generated by shadow, we show that the detection
scheme is effective with false positive rate of 0.001, while sensitivity
detecting non-targets was 0.016+-0.127. Our algorithm could assist
collaborating organisations willing to share their threat intelligence or
cooperate during investigations.Comment: 26 page
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