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Kristin E. Lauter

Bio: Kristin E. Lauter is an academic researcher from Microsoft. The author has contributed to research in topic(s): Encryption & Elliptic curve. The author has an hindex of 44, co-authored 285 publication(s) receiving 11381 citation(s). Previous affiliations of Kristin E. Lauter include University of Texas System & University of Michigan.
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Book ChapterDOI
Seny Kamara1, Kristin E. Lauter1Institutions (1)
25 Jan 2010
TL;DR: This work considers the problem of building a secure cloud storage service on top of a public cloud infrastructure where the service provider is not completely trusted by the customer and describes several architectures that combine recent and non-standard cryptographic primitives to achieve this goal.
Abstract: We consider the problem of building a secure cloud storage service on top of a public cloud infrastructure where the service provider is not completely trusted by the customer We describe, at a high level, several architectures that combine recent and non-standard cryptographic primitives in order to achieve our goal We survey the benefits such an architecture would provide to both customers and service providers and give an overview of recent advances in cryptography motivated specifically by cloud storage

1,271 citations


Proceedings Article
Nathan Dowlin1, Ran Gilad-Bachrach2, Kim Laine2, Kristin E. Lauter2  +2 moreInstitutions (2)
19 Jun 2016
TL;DR: It is shown that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form, which allows high throughput, accurate, and private predictions.
Abstract: Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud-based machine learning solutions for such tasks. In this work, we will present a method to convert learned neural networks to CryptoNets, neural networks that can be applied to encrypted data. This allows a data owner to send their data in an encrypted form to a cloud service that hosts the network. The encryption ensures that the data remains confidential since the cloud does not have access to the keys needed to decrypt it. Nevertheless, we will show that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form. These encrypted predictions can be sent back to the owner of the secret key who can decrypt them. Therefore, the cloud service does not gain any information about the raw data nor about the prediction it made. We demonstrate CryptoNets on the MNIST optical character recognition tasks. CryptoNets achieve 99% accuracy and can make around 59000 predictions per hour on a single PC. Therefore, they allow high throughput, accurate, and private predictions.

961 citations


Proceedings ArticleDOI
21 Oct 2011
TL;DR: A proof-of-concept implementation of the recent somewhat homomorphic encryption scheme of Brakerski and Vaikuntanathan, whose security relies on the "ring learning with errors" (Ring LWE) problem, and a number of application-specific optimizations to the encryption scheme, including the ability to convert between different message encodings in a ciphertext.
Abstract: The prospect of outsourcing an increasing amount of data storage and management to cloud services raises many new privacy concerns for individuals and businesses alike. The privacy concerns can be satisfactorily addressed if users encrypt the data they send to the cloud. If the encryption scheme is homomorphic, the cloud can still perform meaningful computations on the data, even though it is encrypted.In fact, we now know a number of constructions of fully homomorphic encryption schemes that allow arbitrary computation on encrypted data. In the last two years, solutions for fully homomorphic encryption have been proposed and improved upon, but it is hard to ignore the elephant in the room, namely efficiency -- can homomorphic encryption ever be efficient enough to be practical? Certainly, it seems that all known fully homomorphic encryption schemes have a long way to go before they can be used in practice. Given this state of affairs, our contribution is two-fold.First, we exhibit a number of real-world applications, in the medical, financial, and the advertising domains, which require only that the encryption scheme is "somewhat" homomorphic. Somewhat homomorphic encryption schemes, which support a limited number of homomorphic operations, can be much faster, and more compact than fully homomorphic encryption schemes.Secondly, we show a proof-of-concept implementation of the recent somewhat homomorphic encryption scheme of Brakerski and Vaikuntanathan, whose security relies on the "ring learning with errors" (Ring LWE) problem. The scheme is very efficient, and has reasonably short ciphertexts. Our unoptimized implementation in magma enjoys comparable efficiency to even optimized pairing-based schemes with the same level of security and homomorphic capacity. We also show a number of application-specific optimizations to the encryption scheme, most notably the ability to convert between different message encodings in a ciphertext.

933 citations


Book ChapterDOI
01 Nov 2007
Abstract: Recent work by Krawczyk [12] and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols In this paper we focus on security models for authenticated key exchange (AKE) protocols We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model Some of these additional attacks have already been considered by Krawczyk [12] In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary Our contribution is a more compact, integrated, and comprehensive formulation of the security model We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger adversaries

552 citations


Proceedings ArticleDOI
13 Nov 2009
TL;DR: It is shown that an efficient system that allows patients both to share partial access rights with others, and to perform searches over their records is built, based on existing cryptographic primitives and protocols, each achieving a different set of properties.
Abstract: We explore the challenge of preserving patients' privacy in electronic health record systems. We argue that security in such systems should be enforced via encryption as well as access control. Furthermore, we argue for approaches that enable patients to generate and store encryption keys, so that the patients' privacy is protected should the host data center be compromised. The standard argument against such an approach is that encryption would interfere with the functionality of the system. However, we show that we can build an efficient system that allows patients both to share partial access rights with others, and to perform searches over their records. We formalize the requirements of a Patient Controlled Encryption scheme, and give several instantiations, based on existing cryptographic primitives and protocols, each achieving a different set of properties.

453 citations


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Book
01 Jan 2004
TL;DR: This guide explains the basic mathematics, describes state-of-the-art implementation methods, and presents standardized protocols for public-key encryption, digital signatures, and key establishment, as well as side-channel attacks and countermeasures.
Abstract: After two decades of research and development, elliptic curve cryptography now has widespread exposure and acceptance. Industry, banking, and government standards are in place to facilitate extensive deployment of this efficient public-key mechanism. Anchored by a comprehensive treatment of the practical aspects of elliptic curve cryptography (ECC), this guide explains the basic mathematics, describes state-of-the-art implementation methods, and presents standardized protocols for public-key encryption, digital signatures, and key establishment. In addition, the book addresses some issues that arise in software and hardware implementation, as well as side-channel attacks and countermeasures. Readers receive the theoretical fundamentals as an underpinning for a wealth of practical and accessible knowledge about efficient application. Features & Benefits: * Breadth of coverage and unified, integrated approach to elliptic curve cryptosystems * Describes important industry and government protocols, such as the FIPS 186-2 standard from the U.S. National Institute for Standards and Technology * Provides full exposition on techniques for efficiently implementing finite-field and elliptic curve arithmetic* Distills complex mathematics and algorithms for easy understanding* Includes useful literature references, a list of algorithms, and appendices on sample parameters, ECC standards, and software toolsThis comprehensive, highly focused reference is a useful and indispensable resource for practitioners, professionals, or researchers in computer science, computer engineering, network design, and network data security.

2,779 citations



01 Jan 2010
Abstract: Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

1,751 citations


Posted Content
Ari Juels, Burton S. Kaliski1Institutions (1)
TL;DR: This paper defines and explores proofs of retrievability (PORs), a POR scheme that enables an archive or back-up service to produce a concise proof that a user can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.
Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

1,655 citations


Proceedings ArticleDOI
22 May 2017
TL;DR: This work quantitatively investigates how machine learning models leak information about the individual data records on which they were trained and empirically evaluates the inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon.
Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

1,543 citations


Network Information
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Performance
Metrics

Author's H-index: 44

No. of papers from the Author in previous years
YearPapers
20217
202010
201910
201818
201719
201615