A Flexible Network Approach to Privacy of Blockchain Transactions

For preserving privacy, blockchains can be equipped with dedicated mechanisms to anonymize participants. However, these mechanism often take only the abstraction layer of blockchains into account whereas observations of the underlying network traffic can reveal the originator of a transaction request. Previous solutions either provide topological privacy that can be broken by attackers controlling a large number of nodes, or offer strong and cryptographic privacy but are inefficient up to practical unusability. Further, there is no flexible way to trade privacy against efficiency to adjust to practical needs. We propose a novel approach that combines existing mechanisms to have quantifiable and adjustable cryptographic privacy which is further improved by augmented statistical measures that prevent frequent attacks with lower resources. This approach achieves flexibility for privacy and efficency requirements of different blockchain use cases.Comment: 6 pages, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS

Statistical privacy-preserving message dissemination for peer-to-peer networks

Concerns for the privacy of communication is widely discussed in research and overall society. For the public financial infrastructure of blockchains, this discussion encompasses the privacy of transaction data and its broadcasting throughout the network. To tackle this problem, we transform a discrete-time protocol for contact networks over infinite trees into a computer network protocol for peer-to-peer networks. Peer-to-peer networks are modeled as organically growing graphs. We show that the distribution of shortest paths in such a network can be modeled using a normal distribution $\mathcal{N}(\mu,\sigma^2).$ We determine statistical estimators for $\mu,\sigma$ via multivariate models. The model behaves logarithmic over the number of nodes n and proportional to an inverse exponential over the number of added edges k. These results facilitate the computation of optimal forwarding probabilities during the dissemination phase for optimal privacy in a limited information environment.Comment: 6 figures, 19 pages, single colum

Unobtrusive monitoring: Statistical dissemination latency estimation in Bitcoin's peer-to-peer network.

The cryptocurrency system Bitcoin uses a peer-to-peer network to distribute new transactions to all participants. For risk estimation and usability aspects of Bitcoin applications, it is necessary to know the time required to disseminate a transaction within the network. Unfortunately, this time is not immediately obvious and hard to acquire. Measuring the dissemination latency requires many connections into the Bitcoin network, wasting network resources. Some third parties operate that way and publish large scale measurements. Relying on these measurements introduces a dependency and requires additional trust. This work describes how to unobtrusively acquire reliable estimates of the dissemination latencies for transactions without involving a third party. The dissemination latency is modelled with a lognormal distribution, and we estimate their parameters using a Bayesian model that can be updated dynamically. Our approach provides reliable estimates even when using only eight connections, the minimum connection number used by the default Bitcoin client. We provide an implementation of our approach as well as datasets for modelling and evaluation. Our approach, while slightly underestimating the latency distribution, is largely congruent with observed dissemination latencies

Magnetic Resonance Safety Evaluation of a Novel Alumina Matrix Composite Ceramic Knee and Image Artifact Comparison to a Metal Knee Implant of Analogous Design

Background: Image artifacts caused by metal knee implants in 1.5T and 3T magnetic resonance imaging (MRI) systems complicate imaging-based diagnosis of the peri-implant region after total knee arthroplasty. Alternatively, metal-free knee prostheses could effectively minimize MRI safety hazards and offer the potential for higher quality diagnostic images. Methods: A novel knee arthroplasty device composed of BIOLOX delta, an alumina matrix composite (AMC) ceramic, was tested in a magnetic resonance (MR) environment. American Society for Testing and Materials test methods were used for evaluating magnetically induced displacement force, magnetically induced torque, radiofrequency-induced heating, and MR image artifact. Results: Magnetically induced displacement force and magnetically induced torque results of the AMC ceramic knee indicated that these effects do not pose a known risk in a clinical MR environment, as assessed in a 3T magnetic field. Moreover, minimal radiofrequency-induced heating of the device was observed. In addition, the AMC ceramic knee demonstrated minimal MR image artifacts (7 mm) in comparison to a cobalt-chromium knee (88 mm). The extremely low magnetic susceptibility of AMC (2 ppm) underlines that it is a nonmetallic and nonmagnetic material well suited for the manufacturing of MR Safe orthopaedic implants. Conclusions: The AMC ceramic knee is a novel metal-free total knee arthroplasty device that can be regarded as MR Safe, as suggested by the absence of hazards from the exposure of this implant to a MR environment. The AMC ceramic knee presents the advantage of being scanned with superior imaging results in 3T MRI systems compared to alternative metal implants on the market