807 research outputs found
Delegating Quantum Computation in the Quantum Random Oracle Model
A delegation scheme allows a computationally weak client to use a server's
resources to help it evaluate a complex circuit without leaking any information
about the input (other than its length) to the server. In this paper, we
consider delegation schemes for quantum circuits, where we try to minimize the
quantum operations needed by the client. We construct a new scheme for
delegating a large circuit family, which we call "C+P circuits". "C+P" circuits
are the circuits composed of Toffoli gates and diagonal gates. Our scheme is
non-interactive, requires very little quantum computation from the client
(proportional to input length but independent of the circuit size), and can be
proved secure in the quantum random oracle model, without relying on additional
assumptions, such as the existence of fully homomorphic encryption. In practice
the random oracle can be replaced by an appropriate hash function or block
cipher, for example, SHA-3, AES.
This protocol allows a client to delegate the most expensive part of some
quantum algorithms, for example, Shor's algorithm. The previous protocols that
are powerful enough to delegate Shor's algorithm require either many rounds of
interactions or the existence of FHE. The protocol requires asymptotically
fewer quantum gates on the client side compared to running Shor's algorithm
locally.
To hide the inputs, our scheme uses an encoding that maps one input qubit to
multiple qubits. We then provide a novel generalization of classical garbled
circuits ("reversible garbled circuits") to allow the computation of Toffoli
circuits on this encoding. We also give a technique that can support the
computation of phase gates on this encoding.
To prove the security of this protocol, we study key dependent message(KDM)
security in the quantum random oracle model. KDM security was not previously
studied in quantum settings.Comment: 41 pages, 1 figures. Update to be consistent with the proceeding
versio
Secure set-based policy checking and its application to password registration
Policies are the corner stones of today's computer systems. They define secure states and safe operations. A common problem with policies is that their enforcement is often in con ict with user privacy. In order to check the satisfiability of a policy, a server usually needs to collect from a client some information which may be private. In this work we introduce the notion of secure set-based policy checking (SPC) that allows the server to verify policies while preserving the client's privacy. SPC is a generic protocol that can be applied in many policy-based systems. As an example, we show how to use SPC to build a password registration protocol so that a server can check whether a client's password is compliant with its password policy without seeing the password. We also analyse SPC and the password registration protocol and provide security proofs. To demonstrate the practicality of the proposed primitives, we report performance evaluation results based on a prototype implementation of the password registration protoco
Succinct Blind Quantum Computation Using a Random Oracle
In the universal blind quantum computation problem, a client wants to make
use of a single quantum server to evaluate where is an
arbitrary quantum circuit while keeping secret. The client's goal is to use
as few resources as possible. This problem, first raised by Broadbent,
Fitzsimons and Kashefi [FOCS09, arXiv:0807.4154], has become fundamental to the
study of quantum cryptography, not only because of its own importance, but also
because it provides a testbed for new techniques that can be later applied to
related problems (for example, quantum computation verification). Known
protocols on this problem are mainly either information-theoretically (IT)
secure or based on trapdoor assumptions (public key encryptions).
In this paper we study how the availability of symmetric-key primitives,
modeled by a random oracle, changes the complexity of universal blind quantum
computation. We give a new universal blind quantum computation protocol.
Similar to previous works on IT-secure protocols (for example, BFK [FOCS09,
arXiv:0807.4154]), our protocol can be divided into two phases. In the first
phase the client prepares some quantum gadgets with relatively simple quantum
gates and sends them to the server, and in the second phase the client is
entirely classical -- it does not even need quantum storage. Crucially, the
protocol's first phase is succinct, that is, its complexity is independent of
the circuit size. Given the security parameter , its complexity is only
a fixed polynomial of , and can be used to evaluate any circuit (or
several circuits) of size up to a subexponential of . In contrast,
known schemes either require the client to perform quantum computations that
scale with the size of the circuit [FOCS09, arXiv:0807.4154], or require
trapdoor assumptions [Mahadev, FOCS18, arXiv:1708.02130].Comment: 231 pages, 8 figures, 1 table. Add a separate section for extended
technical overview; several readability improvement
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
Implementation of a Secure Multiparty Computation Protocol
Secure multiparty computation (SMC) allows a set of parties to jointly compute a function on private inputs such that, they learn only the output of the function, and the correctness of the output is guaranteed even when a subset of the parties is controlled by an adversary. SMC allows data to be kept in an uncompromisable form and still be useful, and it also gives new meaning to data ownership, allowing data to be shared in a useful way while retaining its privacy. Thus, applications of SMC hold promise for addressing some of the security issues information-driven societies struggle with.
In this thesis, we implement two SMC protocols. Our primary objective is to gain a solid understanding of the basic concepts related to SMC. We present a brief survey of the field, with focus on SMC based on secret sharing. In addition to the protocol im- plementations, we implement circuit randomization, a common technique for efficiency improvement. The implemented protocols are run on a simulator to securely evaluate some simple arithmetic functions, and the round complexities of the implemented protocols are compared. Finally, we attempt to extend the implementation to support more general computations
Fuzzy Password-Authenticated Key Exchange
Consider key agreement by two parties who start out knowing a common secret (which we refer to as “pass-string”, a generalization of “password”), but face two complications: (1) the pass-string may come from a low-entropy distribution, and (2) the two parties’ copies of the pass-string may have some noise, and thus not match exactly. We provide the first efficient and general solutions to this problem that enable, for example, key agreement based on commonly used biometrics such as iris scans.
The problem of key agreement with each of these complications individually has been well studied in literature. Key agreement from low-entropy shared pass-strings is achieved by password-authenticated key exchange (PAKE), and key agreement from noisy but high-entropy shared pass-strings is achieved by information-reconciliation protocols as long as the two secrets are “close enough.” However, the problem of key agreement from noisy low-entropy pass-strings has never been studied.
We introduce (universally composable) fuzzy password-authenticated key exchange (fPAKE), which solves exactly this problem. fPAKE does not have any entropy requirements for the pass-strings, and enables secure key agreement as long as the two pass-strings are “close” for some notion of closeness. We also give two constructions. The first construction achieves our fPAKE definition for any (efficiently computable) notion of closeness, including those that could not be handled before even in the high-entropy setting. It uses Yao’s garbled circuits in a way that is only two times more costly than their use against semi-honest adversaries, but that guarantees security against malicious adversaries. The second construction is more efficient, but achieves our fPAKE definition only for pass-strings with low Hamming distance. It builds on very simple primitives: robust secret sharing and PAKE
Towards Improved Homomorphic Encryption for Privacy-Preserving Deep Learning
Mención Internacional en el título de doctorDeep Learning (DL) has supposed a remarkable transformation for many fields, heralded
by some as a new technological revolution. The advent of large scale models has increased
the demands for data and computing platforms, for which cloud computing has become
the go-to solution. However, the permeability of DL and cloud computing are reduced
in privacy-enforcing areas that deal with sensitive data. These areas imperatively call for
privacy-enhancing technologies that enable responsible, ethical, and privacy-compliant
use of data in potentially hostile environments.
To this end, the cryptography community has addressed these concerns with what
is known as Privacy-Preserving Computation Techniques (PPCTs), a set of tools that
enable privacy-enhancing protocols where cleartext access to information is no longer
tenable. Of these techniques, Homomorphic Encryption (HE) stands out for its ability
to perform operations over encrypted data without compromising data confidentiality or
privacy. However, despite its promise, HE is still a relatively nascent solution with efficiency
and usability limitations. Improving the efficiency of HE has been a longstanding
challenge in the field of cryptography, and with improvements, the complexity of the
techniques has increased, especially for non-experts.
In this thesis, we address the problem of the complexity of HE when applied to DL.
We begin by systematizing existing knowledge in the field through an in-depth analysis
of state-of-the-art for privacy-preserving deep learning, identifying key trends, research
gaps, and issues associated with current approaches. One such identified gap lies in the
necessity for using vectorized algorithms with Packed Homomorphic Encryption (PaHE),
a state-of-the-art technique to reduce the overhead of HE in complex areas. This thesis
comprehensively analyzes existing algorithms and proposes new ones for using DL with
PaHE, presenting a formal analysis and usage guidelines for their implementation.
Parameter selection of HE schemes is another recurring challenge in the literature,
given that it plays a critical role in determining not only the security of the instantiation
but also the precision, performance, and degree of security of the scheme. To address
this challenge, this thesis proposes a novel system combining fuzzy logic with linear
programming tasks to produce secure parametrizations based on high-level user input
arguments without requiring low-level knowledge of the underlying primitives.
Finally, this thesis describes HEFactory, a symbolic execution compiler designed to
streamline the process of producing HE code and integrating it with Python. HEFactory
implements the previous proposals presented in this thesis in an easy-to-use tool. It provides
a unique architecture that layers the challenges associated with HE and produces
simplified operations interpretable by low-level HE libraries. HEFactory significantly reduces
the overall complexity to code DL applications using HE, resulting in an 80% length
reduction from expert-written code while maintaining equivalent accuracy and efficiency.El aprendizaje profundo ha supuesto una notable transformación para muchos campos
que algunos han calificado como una nueva revolución tecnológica. La aparición de modelos
masivos ha aumentado la demanda de datos y plataformas informáticas, para lo cual,
la computación en la nube se ha convertido en la solución a la que recurrir. Sin embargo,
la permeabilidad del aprendizaje profundo y la computación en la nube se reduce en los
ámbitos de la privacidad que manejan con datos sensibles. Estas áreas exigen imperativamente
el uso de tecnologías de mejora de la privacidad que permitan un uso responsable,
ético y respetuoso con la privacidad de los datos en entornos potencialmente hostiles.
Con este fin, la comunidad criptográfica ha abordado estas preocupaciones con las
denominadas técnicas de la preservación de la privacidad en el cómputo, un conjunto de
herramientas que permiten protocolos de mejora de la privacidad donde el acceso a la información
en texto claro ya no es sostenible. Entre estas técnicas, el cifrado homomórfico
destaca por su capacidad para realizar operaciones sobre datos cifrados sin comprometer
la confidencialidad o privacidad de la información. Sin embargo, a pesar de lo prometedor
de esta técnica, sigue siendo una solución relativamente incipiente con limitaciones
de eficiencia y usabilidad. La mejora de la eficiencia del cifrado homomórfico en la
criptografía ha sido todo un reto, y, con las mejoras, la complejidad de las técnicas ha
aumentado, especialmente para los usuarios no expertos.
En esta tesis, abordamos el problema de la complejidad del cifrado homomórfico
cuando se aplica al aprendizaje profundo. Comenzamos sistematizando el conocimiento
existente en el campo a través de un análisis exhaustivo del estado del arte para el aprendizaje
profundo que preserva la privacidad, identificando las tendencias clave, las lagunas
de investigación y los problemas asociados con los enfoques actuales. Una de las
lagunas identificadas radica en el uso de algoritmos vectorizados con cifrado homomórfico
empaquetado, que es una técnica del estado del arte que reduce el coste del cifrado
homomórfico en áreas complejas. Esta tesis analiza exhaustivamente los algoritmos existentes
y propone nuevos algoritmos para el uso de aprendizaje profundo utilizando cifrado
homomórfico empaquetado, presentando un análisis formal y unas pautas de uso para su
implementación.
La selección de parámetros de los esquemas del cifrado homomórfico es otro reto recurrente
en la literatura, dado que juega un papel crítico a la hora de determinar no sólo la
seguridad de la instanciación, sino también la precisión, el rendimiento y el grado de seguridad del esquema. Para abordar este reto, esta tesis propone un sistema innovador que
combina la lógica difusa con tareas de programación lineal para producir parametrizaciones
seguras basadas en argumentos de entrada de alto nivel sin requerir conocimientos
de bajo nivel de las primitivas subyacentes.
Por último, esta tesis propone HEFactory, un compilador de ejecución simbólica diseñado
para agilizar el proceso de producción de código de cifrado homomórfico e integrarlo
con Python. HEFactory es la culminación de las propuestas presentadas en esta
tesis, proporcionando una arquitectura única que estratifica los retos asociados con el
cifrado homomórfico, produciendo operaciones simplificadas que pueden ser interpretadas
por bibliotecas de bajo nivel. Este enfoque permite a HEFactory reducir significativamente
la longitud total del código, lo que supone una reducción del 80% en la
complejidad de programación de aplicaciones de aprendizaje profundo que usan cifrado
homomórfico en comparación con el código escrito por expertos, manteniendo una precisión
equivalente.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: María Isabel González Vasco.- Secretario: David Arroyo Guardeño.- Vocal: Antonis Michala
Preventing injection attacks through automated randomization of keywords
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 47-48).SQL injection attacks are a major security issue for database-backed web applications, yet the most common approaches to prevention require a great deal of programmer effort and attention. Even one unchecked vulnerability can lead to the compromise of an entire application and its data. We present a fully automated system for securing applications against SQL injection which can be applied at runtime. Our system mutates SQL keywords in the program's string constants as they are loaded, and instruments the program's database accesses so that we can verify that all keywords in the final query string have been properly mutated, before passing it to the database. We instrument other method calls within the program to ensure correct program operation, despite the fact that its string constants have been mutated. Additionally, we instrument places where the program generates user-visible output to ensure that randomized keyword mutations are never revealed to an attacker.by Daniel M. Willenson.M.Eng.and S.B
Citation Handling for Improved Summarization of Scientific Documents
In this paper we present the first steps toward improving summarization
of scientific documents through citation analysis and parsing. Prior
work (Mohammad et al., 2009) argues that citation texts (sentences that
cite other papers) play a crucial role in automatic summarization of a
topical area, but did not take into account the noise introduced by the
citations themselves. We demonstrate that it is possible to improve
summarization output through careful handling of these citations. We
base our experiments on the application of an improved trimming approach
to summarization of citation texts extracted from Question-Answering and
Dependency-Parsing documents. We demonstrate that confidence scores from
the Stanford NLP Parser (Klein and Manning, 2003) are significantly
improved, and that Trimmer (Zajic et al., 2007), a sentence-compression
tool, is able to generate higher-quality candidates. Our summarization
output is currently used as part of a larger system, Action Science
Explorer (ASE) (Gove, 2011)
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