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Bootstrapping and maintaining trust in the cloud

December 5, 2016
  |
Conference Paper
Author:
Nabil A. Schear
…
Published in:
32nd Annual Computer Security Applications Conf., ACSAC 2016, 5-9 December 2016.
Topic:
computing
R&D area:
R&D group:

Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. Cloud providers do not offer the ability to create hardware-rooted cryptographic identities for IaaS cloud resources or sufficient information to verify the integrity of systems. Trusted computing protocols and hardware like the TPM have long promised a solution to this problem. However, these technologies have not seen broad adoption because of their complexity of implementation, low performance, and lack of compatibility with virtualized environments. In this paper we introduce keylime, a scalable trusted cloud key management system. keylime provides an end-to-end solution for both bootstrapping hardware rooted cryptographic identities for IaaS nodes and for system integrity monitoring of those nodes via periodic attestation. We support these functions in both bare-metal and virtualized IaaS environments using a virtual TPM. keylime provides a clean interface that allows higher level security services like disk encryption or configuration management to leverage trusted computing without being trusted computing aware. We show that our bootstrapping protocol can derive a key in less than two seconds, we can detect system integrity violations in as little as 110ms, and that keylime can scale to thousands of IaaS cloud nodes.
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Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. Cloud providers do not offer the ability to create hardware-rooted cryptographic identities for IaaS cloud resources or sufficient information to verify the integrity of systems. Trusted computing protocols and hardware...

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Bootstrapping and maintaining trust in the cloud

Leveraging data provenance to enhance cyber resilience

November 3, 2016
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Conference Paper
Author:
Thomas M. Moyer
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Published in:
IEEE Cybersecurity Development, SECDEV 2016, 3-4 November 2016.
Topic:
systems architecture
R&D area:
R&D group:

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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Leveraging data provenance to enhance cyber resilience

Building low-power trustworthy systems: cyber-security considerations for real-time physiological status monitoring

November 1, 2016
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Conference Paper
Author:
John Holliman
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Published in:
IEEE MILCOM, 1-3 November 2016.
Topic:
systems analysis
R&D area:
R&D group:

Summary

Real-time monitoring of physiological data can reduce the likelihood of injury in noncombat military personnel and first-responders. MIT Lincoln Laboratory is developing a tactical Real-Time Physiological Status Monitoring (RT-PSM) system architecture and reference implementation named OBAN (Open Body Area Network), the purpose of which is to provide an open, government-owned framework for integrating multiple wearable sensors and applications. The OBAN implementation accepts data from various sensors enabling calculation of physiological strain information which may be used by squad leaders or medics to assess the team's health and enhance safety and effectiveness of mission execution. Security in terms of measurement integrity, confidentiality, and authenticity is an area of interest because OBAN system components exchange sensitive data in contested environments. In this paper, we analyze potential cyber-security threats and their associated risks to a generalized version of the OBAN architecture and identify directions for future research. The threat analysis is intended to inform the development of secure RT-PSM architectures and implementations.
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Summary

Real-time monitoring of physiological data can reduce the likelihood of injury in noncombat military personnel and first-responders. MIT Lincoln Laboratory is developing a tactical Real-Time Physiological Status Monitoring (RT-PSM) system architecture and reference implementation named OBAN (Open Body Area Network), the purpose of which is to provide an open, government-owned...

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Building low-power trustworthy systems: cyber-security considerations for real-time physiological status monitoring

Side channel authenticity discriminant analysis for device class identification

September 25, 2016
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Conference Paper
Author:
Eric A. Koziel
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Published in:
ICIP: 2016 IEEE Int. Conf. on Image Processing, 25-28 September 2016.
Topic:
systems architecture
R&D area:
R&D group:

Summary

Counterfeit microelectronics present a significant challenge to commercial and defense supply chains. Many modern anti-counterfeit strategies rely on manufacturer cooperation to include additional identification components. We instead propose Side Channel Authenticity Discriminant Analysis (SICADA) to leverage physical phenomena manifesting from device operation to match suspect parts to a class of authentic parts. This paper examines the extent that power dissipation information can be used to separate unique classes of devices. A methodology for distinguishing device types is presented and tested on both simulation data of a custom circuit and empirical measurements of Microchip dsPIC33F microcontrollers. Experimental results show that power side channels contain significant distinguishing information to identify parts as authentic or suspect counterfeit.
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Summary

Counterfeit microelectronics present a significant challenge to commercial and defense supply chains. Many modern anti-counterfeit strategies rely on manufacturer cooperation to include additional identification components. We instead propose Side Channel Authenticity Discriminant Analysis (SICADA) to leverage physical phenomena manifesting from device operation to match suspect parts to a class of...

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Side channel authenticity discriminant analysis for device class identification

High-throughput ingest of data provenance records in Accumulo

September 13, 2016
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Conference Paper
Author:
Thomas M. Moyer
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Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.
Topic:
supercomputing
R&D area:
R&D group:

Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must be stored somewhere where it can be queried. This problem becomes even more challenging when considering a network of provenance-aware machines all collecting this metadata. In this paper, we investigate the use of D4M and Accumulo to support high-throughput data ingest of whole-system provenance data. We find that we are able to ingest 3,970 graph components per second. Centrally storing the provenance metadata allows us to build systems that can detect and respond to data integrity attacks that are captured by the provenance system.
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Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must...

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High-throughput ingest of data provenance records in Accumulo

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

July 7, 2016
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Technical Report
Author:
Gene Itkis
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Published in:
MIT Lincoln Laboratory Report TR-1210
Topic:
cryptography
R&D area:
R&D group:

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

LLMapReduce: multi-level map-reduce for high performance data analysis

May 23, 2016
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Conference Paper
Author:
Chansup Byun
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Published in:
IEEE Int. Parallel and Distributed Processing Symp., IPDPS 2016, 23-27 May 2016.
Topic:
high performance computing
R&D area:
R&D group:

Summary

The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing simple parallel programming capability in one line of code. LLMapReduce supports all programming languages and many schedulers. LLMapReduce can work with any application without the need to modify the application. Furthermore, LLMapReduce can overcome scaling limits in the map-reduce parallel programming model via options that allow the user to switch to the more efficient single-program-multiple-data (SPMD) parallel programming model. These features allow users to reduce the computational overhead by more than 10x compared to standard map-reduce for certain applications. LLMapReduce is widely used by hundreds of users at MIT. Currently LLMapReduce works with several schedulers such as SLURM, Grid Engine and LSF.
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Summary

The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing...

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LLMapReduce: multi-level map-reduce for high performance data analysis

A key-centric processor architecture for secure computing

May 3, 2016
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Conference Paper
Author:
David J. Whelihan
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Published in:
2016 IEEE Int. Symp. on Hardware-Oriented Security and Trust, HOST 2016, 3-5 May 2016.
Topic:
cryptography
R&D area:
R&D group:

Summary

We describe a novel key-centric processor architecture in which each piece of data or code can be protected by encryption while at rest, in transit, and in use. Using embedded key management for cryptographic key handling, our processor permits mutually distrusting software written by different entities to work closely together without divulging algorithmic parameters or secret program data. Since the architecture performs encryption, decryption, and key management deeply within the processor hardware, the attack surface is minimized without significant impact on performance or ease of use. The current prototype implementation is based on the Sparc architecture and is highly applicable to small to medium-sized processing loads.
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Summary

We describe a novel key-centric processor architecture in which each piece of data or code can be protected by encryption while at rest, in transit, and in use. Using embedded key management for cryptographic key handling, our processor permits mutually distrusting software written by different entities to work closely together...

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A key-centric processor architecture for secure computing

Cryptography for Big Data security

April 27, 2016
  |
Book Chapter
Author:
Ariel T. Hamlin
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Published in:
Chapter 10 in Big Data: Storage, Sharing, and Security, 2016, pp. 214-87.
Topic:
cryptography
R&D area:
R&D group:

Summary

This chapter focuses on state-of-the-art provably secure cryptographic techniques for protecting big data applications. We do not focus on more established, and commonly available cryptographic solutions. The goal is to inform practitioners of new techniques to consider as they develop new big data solutions rather than to summarize the current best practice for securing data.
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Summary

This chapter focuses on state-of-the-art provably secure cryptographic techniques for protecting big data applications. We do not focus on more established, and commonly available cryptographic solutions. The goal is to inform practitioners of new techniques to consider as they develop new big data solutions rather than to summarize the current...

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Cryptography for Big Data security

SoK: privacy on mobile devices - it's complicated

March 2, 2016
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Conference Paper
Author:
Chad S. Spensky
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Published in:
Proc. on Privacy Enhancing Techn., Vol. 3, 2016, pp. 96-116.
Topic:
cryptography
R&D area:
R&D group:

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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SoK: privacy on mobile devices - it's complicated
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