134 research outputs found
A secure IoT cloud storage system with fine-grained access control and decryption key exposure resistance
Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in IoT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user secret keys due to different reasons. In this paper, we present a practical attribute-based access control system for IoT cloud by introducing an efficient revocable attribute-based encryption scheme that permits the data owner to efficiently manage the credentials of data users. Our proposed system can efficiently deal with both secret key revocation for corrupted users and accidental decryption key exposure for honest users. We analyze the security of our scheme with formal proofs, and demonstrate the high performance of the proposed system via experiments
Privacy-preserving outsourced support vector machine design for secure drug discovery
AXA Research Fund, Singapore Management Universit
Case-Aware Adversarial Training
The neural network (NN) becomes one of the most heated type of models in
various signal processing applications. However, NNs are extremely vulnerable
to adversarial examples (AEs). To defend AEs, adversarial training (AT) is
believed to be the most effective method while due to the intensive
computation, AT is limited to be applied in most applications. In this paper,
to resolve the problem, we design a generic and efficient AT improvement
scheme, namely case-aware adversarial training (CAT). Specifically, the
intuition stems from the fact that a very limited part of informative samples
can contribute to most of model performance. Alternatively, if only the most
informative AEs are used in AT, we can lower the computation complexity of AT
significantly as maintaining the defense effect. To achieve this, CAT achieves
two breakthroughs. First, a method to estimate the information degree of
adversarial examples is proposed for AE filtering. Second, to further enrich
the information that the NN can obtain from AEs, CAT involves a weight
estimation and class-level balancing based sampling strategy to increase the
diversity of AT at each iteration. Extensive experiments show that CAT is
faster than vanilla AT by up to 3x while achieving competitive defense effect
Lightweight break-glass access control system for healthcare Internet-of-Things
National Research Foundation (NRF) Singapor
Multi-user multi-keyword rank search over encrypted data in arbitrary language
National Research Foundation (NRF) Singapore; AXA Research Fun
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