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Leveraging data provenance to enhance cyber resilience

Summary

Building secure systems used to mean ensuring a secure perimeter, but that is no longer the case. Today's systems are ill-equipped to deal with attackers that are able to pierce perimeter defenses. Data provenance is a critical technology in building resilient systems that will allow systems to recover from attackers that manage to overcome the "hard-shell" defenses. In this paper, we provide background information on data provenance, details on provenance collection, analysis, and storage techniques and challenges. Data provenance is situated to address the challenging problem of allowing a system to "fight-through" an attack, and we help to identify necessary work to ensure that future systems are resilient.
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Summary

Building secure systems used to mean ensuring a secure perimeter, but that is no longer the case. Today's systems are ill-equipped to deal with attackers that are able to pierce perimeter defenses. Data provenance is a critical technology in building resilient systems that will allow systems to recover from attackers...

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Charting a security landscape in the clouds: data protection and collaboration in cloud storage

Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers. We propose a metric for comparing secure storage approaches based on their residual vulnerabilities: attack surfaces against which an approach cannot protect. Our categorization therefore ranks approaches from the weakest (the most residual vulnerabilities) to the strongest (the fewest residual vulnerabilities). In addition to the security provided by each approach, we also consider their inherent costs and limitations. This report can therefore help an organization select a cloud data protection approach that satisfies their enterprise infrastructure, security specifications, and functionality requirements.
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Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers. We propose a metric for...

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SoK: privacy on mobile devices - it's complicated

Summary

Modern mobile devices place a wide variety of sensors and services within the personal space of their users. As a result, these devices are capable of transparently monitoring many sensitive aspects of these users' lives (e.g., location, health, or correspondences). Users typically trade access to this data for convenient applications and features, in many cases without a full appreciation of the nature and extent of the information that they are exposing to a variety of third parties. Nevertheless, studies show that users remain concerned about their privacy and vendors have similarly been increasing their utilization of privacy-preserving technologies in these devices. Still, despite significant efforts, these technologies continue to fail in fundamental ways, leaving users' private data exposed. In this work, we survey the numerous components of mobile devices, giving particular attention to those that collect, process, or protect users' private data. Whereas the individual components have been generally well studied and understood, examining the entire mobile device ecosystem provides significant insights into its overwhelming complexity. The numerous components of this complex ecosystem are frequently built and controlled by different parties with varying interests and incentives. Moreover, most of these parties are unknown to the typical user. The technologies that are employed to protect the users' privacy typically only do so within a small slice of this ecosystem, abstracting away the greater complexity of the system. Our analysis suggests that this abstracted complexity is the major cause of many privacy-related vulnerabilities, and that a fundamentally new, holistic, approach to privacy is needed going forward. We thus highlight various existing technology gaps and propose several promising research directions for addressing and reducing this complexity.
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Summary

Modern mobile devices place a wide variety of sensors and services within the personal space of their users. As a result, these devices are capable of transparently monitoring many sensitive aspects of these users' lives (e.g., location, health, or correspondences). Users typically trade access to this data for convenient applications...

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Iris biometric security challenges and possible solutions: for your eyes only? Using the iris as a key

Summary

Biometrics were originally developed for identification, such as for criminal investigations. More recently, biometrics have been also utilized for authentication. Most biometric authentication systems today match a user's biometric reading against a stored reference template generated during enrollment. If the reading and the template are sufficiently close, the authentication is considered successful and the user is authorized to access protected resources. This binary matching approach has major inherent vulnerabilities. An alternative approach to biometric authentication proposes to use fuzzy extractors (also known as biometric cryptosystems), which derive cryptographic keys from noisy sources, such as biometrics. In theory, this approach is much more robust and can enable cryptographic authorization. Unfortunately, for many biometrics that provide high-quality identification, fuzzy extractors provide no security guarantees. This gap arises in part because of an objective mismatch. The quality of a biometric identification is typically measured using false match rate (FMR) versus false nonmatch rate (FNMR). As a result, biometrics have been extensively optimized for this metric. However, this metric says little about the suitability of a biometric for key derivation. In this article, we illustrate a metric that can be used to optimize biometrics for authentication. Using iris biometrics as an example, we explore possible directions for improving processing and representation according to this metric. Finally, we discuss why strong biometric authentication remains a challenging problem and propose some possible future directions for addressing these challenges.
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Summary

Biometrics were originally developed for identification, such as for criminal investigations. More recently, biometrics have been also utilized for authentication. Most biometric authentication systems today match a user's biometric reading against a stored reference template generated during enrollment. If the reading and the template are sufficiently close, the authentication is...

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Cryptographically secure computation

Published in:
Computer, Vol. 48, No. 4, April 2015, pp. 78-81.

Summary

Researchers are making secure multiparty computation--a cryptographic technique that enables information sharing and analysis while keeping sensitive inputs secret--faster and easier to use for application software developers.
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Summary

Researchers are making secure multiparty computation--a cryptographic technique that enables information sharing and analysis while keeping sensitive inputs secret--faster and easier to use for application software developers.

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Automated assessment of secure search systems

Summary

This work presents the results of a three-year project that assessed nine different privacy-preserving data search systems. We detail the design of a software assessment framework that focuses on low system footprint, repeatability, and reusability. A unique achievement of this project was the automation and integration of the entire test process, from the production and execution of tests to the generation of human-readable evaluation reports. We synthesize our experiences into a set of simple mantras that we recommend following in the design of any assessment framework.
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Summary

This work presents the results of a three-year project that assessed nine different privacy-preserving data search systems. We detail the design of a software assessment framework that focuses on low system footprint, repeatability, and reusability. A unique achievement of this project was the automation and integration of the entire test...

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Computing on masked data: a high performance method for improving big data veracity

Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.

Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.
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Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic...

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Computing on masked data: a high performance method for improving big data veracity

Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.

Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.
READ LESS

Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic...

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Robust keys from physical unclonable functions

Published in:
Proc. 2014 IEEE Int. Symp. on Hardware-Oriented Security and Trust, HOST, 6-7 May 2014.

Summary

Weak physical unclonable functions (PUFs) can instantiate read-proof hardware tokens (Tuyls et al. 2006, CHES) where benign variation, such as changing temperature, yields a consistent key, but invasive attempts to learn the key destroy it. Previous approaches evaluate security by measuring how much an invasive attack changes the derived key (Pappu et al. 2002, Science). If some attack insufficiently changes the derived key, an expert must redesign the hardware. An unexplored alternative uses software to enhance token response to known physical attacks. Our approach draws on machine learning. We propose a variant of linear discriminant analysis (LDA), called PUF LDA, which reduces noise levels in PUF instances while enhancing changes from known attacks. We compare PUF LDA with standard techniques using an optical coating PUF and the following feature types: raw pixels, fast Fourier transform, short-time Fourier transform, and wavelets. We measure the true positive rate for valid detection at a 0% false positive rate (no mistakes on samples taken after an attack). PUF LDA improves the true positive rate from 50% on average (with a large variance across PUFs) to near 100%. While a well-designed physical process is irreplaceable, PUF LDA enables system designers to improve the PUF reliability-security tradeoff by incorporating attacks without redesigning the hardware token.
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Summary

Weak physical unclonable functions (PUFs) can instantiate read-proof hardware tokens (Tuyls et al. 2006, CHES) where benign variation, such as changing temperature, yields a consistent key, but invasive attempts to learn the key destroy it. Previous approaches evaluate security by measuring how much an invasive attack changes the derived key...

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Generating client workloads and high-fidelity network traffic for controllable, repeatable experiments in computer security

Published in:
13th Int. Symp. on Recent Advances in Intrusion Detection, 14 September 2010, pp. 218-237.

Summary

Rigorous scientific experimentation in system and network security remains an elusive goal. Recent work has outlined three basic requirements for experiments, namely that hypotheses must be falsifiable, experiments must be controllable, and experiments must be repeatable and reproducible. Despite their simplicity, these goals are difficult to achieve, especially when dealing with client-side threats and defenses, where often user input is required as part of the experiment. In this paper, we present techniques for making experiments involving security and client-side desktop applications like web browsers, PDF readers, or host-based firewalls or intrusion detection systems more controllable and more easily repeatable. First, we present techniques for using statistical models of user behavior to drive real, binary, GUI-enabled application programs in place of a human user. Second, we present techniques based on adaptive replay of application dialog that allow us to quickly and efficiently reproduce reasonable mock-ups of remotely-hosted applications to give the illusion of Internet connectedness on an isolated testbed. We demonstrate the utility of these techniques in an example experiment comparing the system resource consumption of a Windows machine running anti-virus protection versus an unprotected system.
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Summary

Rigorous scientific experimentation in system and network security remains an elusive goal. Recent work has outlined three basic requirements for experiments, namely that hypotheses must be falsifiable, experiments must be controllable, and experiments must be repeatable and reproducible. Despite their simplicity, these goals are difficult to achieve, especially when dealing...

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