Secure Computation Protocols for Privacy-Preserving Machine Learning

Machine Learning (ML) profitiert erheblich von der Verfügbarkeit großer Mengen an Trainingsdaten, sowohl im Bezug auf die Anzahl an Datenpunkten, als auch auf die Anzahl an Features pro Datenpunkt. Es ist allerdings oft weder möglich, noch gewollt, mehr Daten unter zentraler Kontrolle zu aggregieren. Multi-Party-Computation (MPC)-Protokolle stellen eine Lösung dieses Dilemmas in Aussicht, indem sie es mehreren Parteien erlauben, ML-Modelle auf der Gesamtheit ihrer Daten zu trainieren, ohne die Eingabedaten preiszugeben. Generische MPC-Ansätze bringen allerdings erheblichen Mehraufwand in der Kommunikations- und Laufzeitkomplexität mit sich, wodurch sie sich nur beschränkt für den Einsatz in der Praxis eignen. Das Ziel dieser Arbeit ist es, Privatsphäreerhaltendes Machine Learning mittels MPC praxistauglich zu machen. Zuerst fokussieren wir uns auf zwei Anwendungen, lineare Regression und Klassifikation von Dokumenten. Hier zeigen wir, dass sich der Kommunikations- und Rechenaufwand erheblich reduzieren lässt, indem die aufwändigsten Teile der Berechnung durch Sub-Protokolle ersetzt werden, welche auf die Zusammensetzung der Parteien, die Verteilung der Daten, und die Zahlendarstellung zugeschnitten sind. Insbesondere das Ausnutzen dünnbesetzter Datenrepräsentationen kann die Effizienz der Protokolle deutlich verbessern. Diese Beobachtung verallgemeinern wir anschließend durch die Entwicklung einer Datenstruktur für solch dünnbesetzte Daten, sowie dazugehöriger Zugriffsprotokolle. Aufbauend auf dieser Datenstruktur implementieren wir verschiedene Operationen der Linearen Algebra, welche in einer Vielzahl von Anwendungen genutzt werden. Insgesamt zeigt die vorliegende Arbeit, dass MPC ein vielversprechendes Werkzeug auf dem Weg zu Privatsphäre-erhaltendem Machine Learning ist, und die von uns entwickelten Protokolle stellen einen wesentlichen Schritt in diese Richtung dar.Machine learning (ML) greatly benefits from the availability of large amounts of training data, both in terms of the number of samples, and the number of features per sample. However, aggregating more data under centralized control is not always possible, nor desirable, due to security and privacy concerns, regulation, or competition. Secure multi-party computation (MPC) protocols promise a solution to this dilemma, allowing multiple parties to train ML models on their joint datasets while provably preserving the confidentiality of the inputs. However, generic approaches to MPC result in large computation and communication overheads, which limits the applicability in practice. The goal of this thesis is to make privacy-preserving machine learning with secure computation practical. First, we focus on two high-level applications, linear regression and document classification. We show that communication and computation overhead can be greatly reduced by identifying the costliest parts of the computation, and replacing them with sub-protocols that are tailored to the number and arrangement of parties, the data distribution, and the number representation used. One of our main findings is that exploiting sparsity in the data representation enables considerable efficiency improvements. We go on to generalize this observation, and implement a low-level data structure for sparse data, with corresponding secure access protocols. On top of this data structure, we develop several linear algebra algorithms that can be used in a wide range of applications. Finally, we turn to improving a cryptographic primitive named vector-OLE, for which we propose a novel protocol that helps speed up a wide range of secure computation tasks, within private machine learning and beyond. Overall, our work shows that MPC indeed offers a promising avenue towards practical privacy-preserving machine learning, and the protocols we developed constitute a substantial step in that direction

Search for eV Sterile Neutrinos -- The STEREO Experiment [TAUP 2017]

In the recent years, major milestones in neutrino physics were accomplished at nuclear reactors: the smallest neutrino mixing angle $\theta_{13}$ was determined with high precision and the emitted antineutrino spectrum was measured at unprecedented resolution. However, two anomalies, the first one related to the absolute flux and the second one to the spectral shape, have yet to be solved. The flux anomaly is known as the Reactor Antineutrino Anomaly and could be caused by the existence of a light sterile neutrino eigenstate participating in the neutrino oscillation phenomenon. Introducing a sterile state implies the presence of a fourth mass eigenstate, while global fits favour oscillation parameters around $\sin^{2}(2\theta)=0.09$ and $\Delta m^{2}=1.8\textrm{eV}^{2}$. The STEREO experiment was built to finally solve this puzzle. It is one of the first running experiments built to search for eV sterile neutrinos and takes data since end of 2016 at ILL Grenoble, France. At a short baseline of 10 metres, it measures the antineutrino flux and spectrum emitted by a compact research reactor. The segmentation of the detector in six target cells allows for independent measurements of the neutrino spectrum at multiple baselines. An active-sterile flavour oscillation could be unambiguously detected, as it distorts the spectral shape of each cell's measurement differently. This contribution gives an overview on the STEREO experiment, along with details on the detector design, detection principle and the current status of data analysis.Comment: 5 pages, 4 figures, contribution to the proceedings of the TAUP 2017 conferenc

Production and Properties of the Liquid Scintillators used in the Stereo Reactor Neutrino Experiment

The electron antineutrino spectrum in the Stereo reactor experiment (ILL Grenoble) is measured via the inverse beta decay signals in an organic liquid scintillator. The six target cells of the Stereo detector are filled with about 1800 litres of Gd-loaded liquid scintillator optimised for the requirements of the experiment. These target cells are surrounded by similar cells containing liquid scintillator without the Gd-loading. The development and characteristics of these scintillators are reported. In particular, the transparency, light production and pulse shape discrimination capabilities of the organic liquids are discussed.Comment: 10 pages, 4 figure

The operation of eVTOLs in the urban air mobility sector : use case & operator assessment

Electric vertical takeoff and landing (eVTOL) technology enables a sustainable form of aviation for currently unserved connections. eVTOLs can reduce the carbon emissions of the aviation industry by replacing conventional helicopters and smaller aircrafts. In addition, traffic congestion in large cities can be decreased and travel times shortened. This thesis examines which use case is offered to the passengers. In addition, the prospective urban air mobility (UAM) market player that is expected to become a potential operator in the short term is summarized, alongside the value proposition (VP), key resources (KR), key activities (KA), and key partnerships (KP) of an eVTOL operator. The findings have been obtained through a qualitative research approach questioning 16 UAM experts, commercial as well as private aviation companies. The results reveal that eVTOL manufacturers are going to be the first market players to operate eVTOLs in the short to medium term. Commercial airlines are expected to step into the market in the long term. Interviews have additionally shown that airport shuttles will be the first served use case. The primary value proposition is saving time for the passengers. The primary key resource is human staff. The primary key activity is eVTOL maintenance. The primary key partnership is with the infrastructure provider and/or operator. Furthermore, this research adds value to the existing UAM literature on eVTOL operators, first commercially offered use cases as well as elements of an operator’s business model.A tecnologia de decolagem e aterrissagem vertical elétrica (eVTOL) permite uma forma sustentável de aviação para conexões atualmente não atendidas. Os eVTOLs podem reduzir as emissões de carbono da indústria da aviação substituindo helicópteros convencionais e aeronaves menores. Além disso, o congestionamento do tráfego nas grandes cidades pode ser reduzido e os tempos de viagem reduzidos. Esta tese examina qual caso de uso é oferecido aos passageiros. Além disso, o potencial participante do mercado de mobilidade aérea urbana (UAM) que deve se tornar um operador potencial no curto prazo é resumido, juntamente com a proposta de valor (VP), recursos-chave (KR), atividades-chave (KA) e parcerias (KP) de um operador eVTOL. Os resultados foram obtidos por meio de uma abordagem de pesquisa qualitativa questionando 16 especialistas, empresas de aviação comercial e privada. Os resultados revelam que os fabricantes de eVTOL serão os primeiros players do mercado a operar eVTOLs no curto e médio prazo. Espera-se que as companhias aéreas comerciais entrem no mercado a longo prazo. As entrevistas também mostraram que os ônibus do aeroporto serão o primeiro caso de uso servido. A principal proposta de valor é economizar tempo para os passageiros. O recurso-chave primário é a equipe humana. A atividade de chave primária é a manutenção do eVTOL. A parceria de chave primária é com o provedor e/ou operador de infraestrutura. Além disso, esta pesquisa agrega valor à literatura UAM existente sobre operadoras eVTOL, primeiros casos de uso oferecidos comercialmente, bem como elementos do modelo de negócios de uma operadora

Polynomial-time computability of the edge-reliability of graphs using Gilbert's formula

Reliability is an important consideration in analyzing computer and other communication networks, but current techniques are extremely limited in the classes of graphs which can be analyzed efficiently. While Gilbert's formula establishes a theoretically elegant recursive relationship between the edge reliability of a graph and the reliability of its subgraphs, naive evaluation requires consideration of all sequences of deletions of individual vertices, and for many graphs has time complexity essentially Θ (N!). We discuss a general approach which significantly reduces complexity, encoding subgraph isomorphism in a finer partition by invariants, and recursing through the set of invariants

Novel Opaque Scintillator for Neutrino Detection

There is rising interest in organic scintillators with low scattering length for future neutrino detectors. Therefore, a new scintillator system was developed based on admixtures of paraffin wax in linear alkyl benzene. The transparency and viscosity of this gel-like material can be tuned by temperature adjustment. Whereas it is a colorless transparent liquid at temperatures around 40C it has a milky wax structure below 20C. The production and properties of such a scintillator as well as its advantages compared to transparent liquids are described.Comment: 11 pages, 6 figure