4,022 research outputs found
Open-TEE - An Open Virtual Trusted Execution Environment
Hardware-based Trusted Execution Environments (TEEs) are widely deployed in
mobile devices. Yet their use has been limited primarily to applications
developed by the device vendors. Recent standardization of TEE interfaces by
GlobalPlatform (GP) promises to partially address this problem by enabling
GP-compliant trusted applications to run on TEEs from different vendors.
Nevertheless ordinary developers wishing to develop trusted applications face
significant challenges. Access to hardware TEE interfaces are difficult to
obtain without support from vendors. Tools and software needed to develop and
debug trusted applications may be expensive or non-existent.
In this paper, we describe Open-TEE, a virtual, hardware-independent TEE
implemented in software. Open-TEE conforms to GP specifications. It allows
developers to develop and debug trusted applications with the same tools they
use for developing software in general. Once a trusted application is fully
debugged, it can be compiled for any actual hardware TEE. Through performance
measurements and a user study we demonstrate that Open-TEE is efficient and
easy to use. We have made Open- TEE freely available as open source.Comment: Author's version of article to appear in 14th IEEE International
Conference on Trust, Security and Privacy in Computing and Communications,
TrustCom 2015, Helsinki, Finland, August 20-22, 201
Remote attestation of SEV-SNP confidential VMs using e-vTPMs
Departing from "your data is safe with us" model where the cloud
infrastructure is trusted, cloud tenants are shifting towards a model in which
the cloud provider is not part of the trust domain. Both silicon and cloud
vendors are trying to address this shift by introducing confidential computing
- an umbrella term that provides mechanisms for protecting the data in-use
through encryption below the hardware boundary of the CPU, e.g., Intel Software
Guard Extensions (SGX), AMD secure encrypted virtualization (SEV), Intel trust
domain extensions (TDX), etc.
In this work, we design and implement a virtual trusted platform module
(vTPM) that virtualizes the hardware root-of-trust without requiring to trust
the cloud provider. To ensure the security of a vTPM in a provider-controlled
environment, we leverage unique isolation properties of the SEV-SNP hardware
and a novel approach to ephemeral TPM state management. Specifically, we
develop a stateless ephemeral vTPM that supports remote attestation without
persistent state. This allows us to pair each confidential VM with a private
instance of a vTPM that is completely isolated from the provider-controlled
environment and other VMs. We built our prototype entirely on open-source
components - Qemu, Linux, and Keylime. Though our work is AMD-specific, a
similar approach could be used to build remote attestation protocol on other
trusted execution environments (TEE).Comment: 12 pages, 4 figure
On Making Emerging Trusted Execution Environments Accessible to Developers
New types of Trusted Execution Environment (TEE) architectures like TrustLite
and Intel Software Guard Extensions (SGX) are emerging. They bring new features
that can lead to innovative security and privacy solutions. But each new TEE
environment comes with its own set of interfaces and programming paradigms,
thus raising the barrier for entry for developers who want to make use of these
TEEs. In this paper, we motivate the need for realizing standard TEE interfaces
on such emerging TEE architectures and show that this exercise is not
straightforward. We report on our on-going work in mapping GlobalPlatform
standard interfaces to TrustLite and SGX.Comment: Author's version of article to appear in 8th Internation Conference
of Trust & Trustworthy Computing, TRUST 2015, Heraklion, Crete, Greece,
August 24-26, 201
ARPA Whitepaper
We propose a secure computation solution for blockchain networks. The
correctness of computation is verifiable even under malicious majority
condition using information-theoretic Message Authentication Code (MAC), and
the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty
computation protocol and a layer2 solution, our privacy-preserving computation
guarantees data security on blockchain, cryptographically, while reducing the
heavy-lifting computation job to a few nodes. This breakthrough has several
implications on the future of decentralized networks. First, secure computation
can be used to support Private Smart Contracts, where consensus is reached
without exposing the information in the public contract. Second, it enables
data to be shared and used in trustless network, without disclosing the raw
data during data-at-use, where data ownership and data usage is safely
separated. Last but not least, computation and verification processes are
separated, which can be perceived as computational sharding, this effectively
makes the transaction processing speed linear to the number of participating
nodes. Our objective is to deploy our secure computation network as an layer2
solution to any blockchain system. Smart Contracts\cite{smartcontract} will be
used as bridge to link the blockchain and computation networks. Additionally,
they will be used as verifier to ensure that outsourced computation is
completed correctly. In order to achieve this, we first develop a general MPC
network with advanced features, such as: 1) Secure Computation, 2) Off-chain
Computation, 3) Verifiable Computation, and 4)Support dApps' needs like
privacy-preserving data exchange
Completely Automated Public Physical test to tell Computers and Humans Apart: A usability study on mobile devices
A very common approach adopted to fight the increasing sophistication and dangerousness of malware and hacking is to introduce more complex authentication mechanisms. This approach, however, introduces additional cognitive burdens for users and lowers the whole authentication mechanism acceptability to the point of making it unusable. On the contrary, what is really needed to fight the onslaught of automated attacks to users data and privacy is to first tell human and computers apart and then distinguish among humans to guarantee correct authentication. Such an approach is capable of completely thwarting any automated attempt to achieve unwarranted access while it allows keeping simple the mechanism dedicated to recognizing the legitimate user. This kind of approach is behind the concept of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), yet CAPTCHA leverages cognitive capabilities, thus the increasing sophistication of computers calls for more and more difficult cognitive tasks that make them either very long to solve or very prone to false negatives. We argue that this problem can be overcome by substituting the cognitive component of CAPTCHA with a different property that programs cannot mimic: the physical nature. In past work we have introduced the Completely Automated Public Physical test to tell Computer and Humans Apart (CAPPCHA) as a way to enhance the PIN authentication method for mobile devices and we have provided a proof of concept implementation. Similarly to CAPTCHA, this mechanism can also be used to prevent automated programs from abusing online services. However, to evaluate the real efficacy of the proposed scheme, an extended empirical assessment of CAPPCHA is required as well as a comparison of CAPPCHA performance with the existing state of the art. To this aim, in this paper we carry out an extensive experimental study on both the performance and the usability of CAPPCHA involving a high number of physical users, and we provide comparisons of CAPPCHA with existing flavors of CAPTCHA
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