Election Security Is Harder Than You Think

Recent years have seen the rise of nation-state interference in elections across the globe, making the ever-present need for more secure elections all the more dire. While certain common-sense approaches have been a typical response in the past, e.g. ``don't connect voting machines to the Internet'' and ``use a voting system with a paper trail'', known-good solutions to improving election security have languished in relative obscurity for decades. These techniques are only now finally being implemented at scale, and that implementation has brought the intricacies of sophisticated approaches to election security into full relief. This dissertation argues that while approaches to improve election security like paper ballots and post-election audits seem straightforward, in reality there are significant practical barriers to sufficient implementation. Overcoming these barriers is a necessary condition for an election to be secure, and while doing so is possible, it requires significant refinement of existing techniques. In order to better understand how election security technology can be improved, I first develop what it means for an election to be secure. I then delve into experimental results regarding voter-verified paper, discussing the challenges presented by paper ballots as well as some strategies to improve the security they can deliver. I examine the post-election audit ecosystem and propose a manifest improvement to audit workload analysis through parallelization. Finally, I show that even when all of these conditions are met (as in a vote-by-mail scenario), there are still wrinkles that must be addressed for an election to be truly secure.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163272/1/matber_1.pd

Protecting User Privacy by Monitoring API Queries

This publication describes a method for protecting user privacy from targeted application interface program (API) queries made to a prediction service on a computing device. More specifically, the method involves utilizing an API Query Manager to monitor API calls made by applications to the prediction service. If the API Query Manager determines a lack of positive feedback (e.g., screen content presented on a display of the computing device does not match the personalized prediction returned from an API call), then the API Query Manager can throttle further API calls made by the application to protect user privacy

Cache as a service:leveraging SDN to efficiently and transparently support Video-on-Demand on the last mile

High quality online video streaming, both live and on-demand, has become an essential part of consumers’ every-day lives. The popularity of video streaming as placed a heavy burden on the network infrastructure that now has to transfer an enormous amount of data very quickly to the end-user. To further exacerbate the situation, the Video-on-Demand (VoD) distribution paradigm uses a unicast independent flow for each user request. This results in multiple duplicate flows carrying the same video assets many times end-to-end. We present OpenCache: a highly configurable, efficient and transparent in-network caching service that aims to improve the VoD distribution efficiency by caching video assets as close to the end-user as possible. OpenCache leverages Software Defined Networking to benefit last mile environments by improving network utilisation and increasing the Quality of Experience for the end-user. Our evaluation on a pan-European OpenFlow testbed uses adaptive video streaming and demonstrates that with the use of OpenCache, the external link utilisation is reduced by 100%. Furthermore the streaming application receives better quality video and observes higher throughput, lower latency and shorter start up and buffering times

Combining high-dispersion spectroscopy (HDS) with high contrast imaging (HCI): Probing rocky planets around our nearest neighbors

Aims: In this work, we discuss a way to combine High Dispersion Spectroscopy and High Contrast Imaging (HDS+HCI). For a planet located at a resolvable angular distance from its host star, the starlight can be reduced up to several orders of magnitude using adaptive optics and/or coronography. In addition, the remaining starlight can be filtered out using high-dispersion spectroscopy, utilizing the significantly different (or Doppler shifted) high-dispersion spectra of the planet and star. In this way, HDS+HCI can in principle reach contrast limits of ~1e-5 x 1e-5, although in practice this will be limited by photon noise and/or sky-background. Methods: We present simulations of HDS+HCI observations with the E-ELT, both probing thermal emission from a planet at infrared wavelengths, and starlight reflected off a planet atmosphere at optical wavelengths. For the infrared simulations we use the baseline parameters of the E-ELT and METIS instrument, with the latter combining extreme adaptive optics with an R=100,000 IFS. We include realistic models of the adaptive optics performance and atmospheric transmission and emission. For the optical simulation we also assume R=100,000 IFS with adaptive optics capabilities at the E-ELT. Results: One night of HDS+HCI observations with the E-ELT at 4.8 um (d_lambda = 0.07 um) can detect a planet orbiting alpha Cen A with a radius of R=1.5 R_earth and a twin-Earth thermal spectrum of T_eq=300 K at a signal-to-noise (S/N) of 5. In the optical, with a Strehl ratio performance of 0.3, reflected light from an Earth-size planet in the habitable zone of Proxima Centauri can be detected at a S/N of 10 in the same time frame. Recently, first HDS+HCI observations have shown the potential of this technique by determining the spin-rotation of the young massive exoplanet beta Pictoris b. [abridged]Comment: 9 pages, A&A in press: A movie of the simulation can be found at http://www.strw.leidenuniv.nl/~snellen/simulation.mpe

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical Imagin