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Register a new userCertificate Linking and Caching for Logical Trust
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Added by
Qiang Cao
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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SAFE is a data-centric platform for building multi-domain networked systems, i.e., systems whose participants are controlled by different principals. Participants make trust decisions by issuing local queries over logic content exchanged in certificates. The contribution of SAFE is to address a key barrier to practical use of logical trust: the problem of identifying, gathering, and assembling the certificates that are relevant to each trust decision. SAFE uses a simple linking abstraction to organize and share certificates according to scripted primitives that implement the applications trust kernel and isolate it from logic concerns. We show that trust scripting with logical data exchange yields compact trust cores for example applications: federated naming, nested groups and roles, secure IP prefix delegation and routing, attestation-based access control, and a federated infrastructure-as-a-service system. Linking allows granular control over dynamic logic content based on dependency relationships, enabling a logic server to make secure inferences at high throughput.
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The salient features of blockchain, such as decentralisation and transparency, have allowed the development of Decentralised Trust and Reputation Management Systems (DTRMS), which mainly aim to quantitatively assess the trustworthiness of the network participants and help to protect the network from adversaries. In the literature, proposals of DTRMS have been applied to various Cyber-physical Systems (CPS) applications, including supply chains, smart cities and distributed energy trading. In this chapter, we outline the building blocks of a generic DTRMS and discuss how it can benefit from blockchain. To highlight the significance of DTRMS, we present the state-of-the-art of DTRMS in various field of CPS applications. In addition, we also outline challenges and future directions in developing DTRMS for CPS.
There has been tremendous interest in the development of formal trust models and metrics through the use of analytics (e.g., Belief Theory and Bayesian models), logics (e.g., Epistemic and Subjective Logic) and other mathematical models. The choice of trust metric will depend on context, circumstance and user requirements and there is no single best metric for use in all circumstances. Where different users require different trust metrics to be employed the trust score calculations should still be based on all available trust evidence. Trust is normally computed using past experiences but, in practice (especially in centralised systems), the validity and accuracy of these experiences are taken for granted. In this paper, we provide a formal framework and practical blockchain-based implementation that allows independent trust providers to implement different trust metrics in a distributed manner while still allowing all trust providers to base their calculations on a common set of trust evidence. Further, our design allows experiences to be provably linked to interactions without the need for a central authority. This leads to the notion of evidence-based trust with provable interactions. Leveraging blockchain allows the trust providers to offer their services in a competitive manner, charging fees while users are provided with payments for recording experiences. Performance details of the blockchain implementation are provided.
The security of TLS depends on trust in certificate authorities, and that trust stems from their ability to protect and control the use of a private signing key. The signing key is the key asset of a certificate authority (CA), and its value is based on trust in the corresponding public key which is primarily distributed by browser vendors. Compromise of a CA private key represents a single point-of-failure that could have disastrous consequences, so CAs go to great lengths to attempt to protect and control the use of their private keys. Nevertheless, keys are sometimes compromised and may be misused accidentally or intentionally by insiders. We propose splitting a CAs private key among multiple parties, and producing signatures using a generic secure multi-party computation protocol that never exposes the actual signing key. This could be used by a single CA to reduce the risk that its signing key would be compromised or misused. It could also enable new models for certificate generation, where multiple CAs would need to agree and cooperate before a new certificate can be generated, or even where certificate generation would require cooperation between a CA and the certificate recipient (subject). Although more efficient solutions are possible with custom protocols, we demonstrate the feasibility of implementing a decentralized CA using a generic two-party secure computation protocol with an evaluation of a prototype implementation that uses secure two-party computation to generate certificates signed using ECDSA on curve secp192k1.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of IoT applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern in the Fog computing environment. Furthermore, mitigating and preventing those security measures is a research issue. Therefore, to provide the necessary security for Fog devices, we need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works needs to be identified and need to be aggregate all issues in Fog security. It needs to be noted that computation devices consist of many ordinary users, and are not managed by any central entity or managing body. Therefore, trust and privacy is also a key challenge to gain market adoption for Fog. To provide the required trust and privacy, we need to also focus on authentication, threats and access control mechanisms as well as techniques in Fog computing. In this paper, we perform a survey and propose a taxonomy, which presents an overview of existing security concerns in the context of the Fog computing paradigm. We discuss the Blockchain-based solutions towards a secure Fog computing environment and presented various research challenges and directions for future research.
In many machine learning applications, one wants to learn the unknown objective and constraint functions of an optimization problem from available data and then apply some technique to attain a local optimizer of the learned model. This work considers Gaussian processes as global surrogate models and utilizes them in conjunction with derivative-free trust-region methods. It is well known that derivative-free trust-region methods converge globally---provided the surrogate model is probabilistically fully linear. We prove that glspl{gp} are indeed probabilistically fully linear, thus resulting in fast (compared to linear or quadratic local surrogate models) and global convergence. We draw upon the optimization of a chemical reactor to demonstrate the efficiency of gls{gp}-based trust-region methods.
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