Exciting new capabilities of modern trusted hardware technologies allow for the execution of arbitrary code within environments completely isolated from the rest of the system and provide cryptographic mechanisms for securely reporting on these executions to remote parties.Rigorously proving security of protocols that rely on this type of hardware faces two obstacles. The first is to develop models appropriate for the induced trust assumptions (e.g., what is the correct notion of a party when the peer one wishes to communicate with is a specific instance of an an outsourced program). The second is to develop scalable analysis methods, as the inherent stateful nature of the platforms precludes the application of existing modular analysis techniques that require high degrees of independence between the components.We give the first steps in this direction by studying three cryptographic tools which have been commonly associated with this new generation of trusted hardware solutions. Specifically, we provide formal security definitions, generic constructions and security analysis for attested computation, key-exchange for attestation and secure outsourced computation. Our approach is incremental: each of the concepts relies on the previous ones according to an approach that is quasi-modular. For example we show how to build a secure outsourced computation scheme from an arbitrary attestation protocol combined together with a key-exchange and an encryption scheme.
Abstract. In this paper we show how Isolated Execution Environments (IEE) offered by novel commodity hardware such as Intel's SGX provide a new path to constructing general secure multiparty computation (MPC) protocols. Our protocol is intuitive and elegant: it uses code within an IEE to play the role of a trusted third party (TTP), and the attestation guarantees of SGX to bootstrap secure communications between participants and the TTP. The load of communications and computations on participants only depends on the size of each party's inputs and outputs and is thus small and independent from the intricacies of the functionality to be computed. The remaining computational load-essentially that of computing the functionality -is moved to an untrusted party running an IEE-enabled machine, an attractive feature for Cloud-based scenarios. Our rigorous modular security analysis relies on the novel notion of labeled attested computation which we put forth in this paper. This notion is a convenient abstraction of the kind of attestation guarantees one can obtain from trusted hardware in multi-user scenarios. Finally, we present an extensive experimental evaluation of our solution on SGXenabled hardware. Our implementation is open-source and it is functionality agnostic: it can be used to securely outsource to the Cloud arbitrary off-the-shelf collaborative software, such as the one employed on financial data applications, enabling secure collaborative execution over private inputs provided by multiple parties. IntroductionSecure multiparty computation (MPC) allows a set of mutually distrusting parties to collaboratively carry out a computation that involves their private inputs. The security guarantee that parties get are essentially those provided by carrying out the same computation using a Trusted Third Party (TTP). The computations to be carried out range from simple functionalities, for example where a party commits to a secret value and later on reveals it; or they can be highly complex, for example running sealed bid auctions [11] or bank customer benchmarking [20]. Most of the existent approaches are software only. The trust barrier between parties is overcome using cryptographic techniques that permit computing over encrypted and/or secret-shared data [35,28,19]. Another approach first studied by Katz [31] formalizes a trusted hardware assumptionwhere users have access to tamper-proof tokens on which they can load arbitrary codethat is sufficient to bootstrap universally composable MPC.Broadly speaking, this work fits within the same category as that by Katz [15]. However, our starting point is a novel real-world form of trusted hardware that is currently shipped on commodity PCs: Intel's Software Guard Extensions [30]. Our goal is to leverage this hardware to significantly reduce the computational costs of practical secure computation protocols. The main security capability that such hardware offers are Isolated Execution Environments (IEE) -a powerful tool for boosting trust in remote systems under the tot...
Cloud infrastructures provide database services as cost-efficient and scalable solutions for storing and processing large amounts of data. To maximize performance, these services require users to trust sensitive information to the cloud provider, which raises privacy and legal concerns. This represents a major obstacle to the adoption of the cloud computing paradigm.Recent work addressed this issue by extending databases to compute over encrypted data. However, these approaches usually support a single and strict combination of cryptographic techniques invariably making them application specific. To assess and broaden the applicability of cryptographic techniques in secure cloud storage and processing, these techniques need to be thoroughly evaluated in a modular and configurable database environment. This is even more noticeable for NoSQL data stores where data privacy is still mostly overlooked.In this paper, we present a generic NoSQL framework and a set of libraries supporting data processing cryptographic techniques that can be used with existing NoSQL engines and composed to meet the privacy and performance requirements of different applications. This is achieved through a modular and extensible design that enables data processing over multiple cryptographic techniques applied on the same database. For each technique, we provide an overview of its security model, along with an extensive set of experiments. The framework is evaluated with the YCSB benchmark, where we assess the practicality and performance tradeoffs for different combinations of cryptographic techniques. The results for a set of macro experiments show that the average overhead in NoSQL operations performance is below 15%, when comparing our system with a baseline database without privacy guarantees.
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