Biblio

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

The explosive proliferation of Internet of Things (IoT) devices is generating an incomprehensible amount of data. Machine learning plays an imperative role in aggregating this data and extracting valuable information for improving operational and decision-making processes. In particular, emerging machine intelligence platforms that host pre-trained machine learning models are opening up new opportunities for IoT industries. While those platforms facilitate customers to analyze IoT data and deliver faster and accurate insights, end users and machine learning service providers (MLSPs) have raised concerns regarding security and privacy of IoT data as well as the pre-trained machine learning models for certain applications such as healthcare, smart energy, etc. In this paper, we propose a cloud-assisted, privacy-preserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with MLSPs on behalf of the resource-constrained IoT devices in a privacy-preserving manner, and shift load of computation-intensive classification operations from them. The detailed security analysis and the extensive simulations with different key lengths and number of features and classes demonstrate that our scheme can effectively reduce the overhead for IoT devices in machine learning classification applications.

With the increased popularity of ubiquitous computing and connectivity, the Internet of Things (IoT) also introduces new vulnerabilities and attack vectors. While secure data collection (i.e. the upward link) has been well studied in the literature, secure data dissemination (i.e. the downward link) remains an open problem. Attribute-based encryption (ABE) and outsourced-ABE has been used for secure message distribution in IoT, however, existing mechanisms suffer from extensive computation and/or privacy issues. In this paper, we explore the problem of privacy-preserving targeted broadcast in IoT. We propose two multi-cloud-based outsourced-ABE schemes, namely the parallel-cloud ABE and the chain-cloud ABE, which enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing user attributes from being disclosed. In particular, the proposed solution protects three types of privacy (i.e., data, attribute and access policy privacy) by enforcing collaborations among multiple clouds. Our schemes also provide delegation verifiability that allows the receivers to verify whether the clouds have faithfully performed the outsourced operations. We extensively analyze the security guarantees of the proposed mechanisms and demonstrate the effectiveness and efficiency of our schemes with simulated resource-constrained IoT devices, which outsource operations to Amazon EC2 and Microsoft Azure.