Biblio

The security of manycore systems has become increasingly critical. In system-on-chips (SoCs), Hardware Trojans (HTs) manipulate the functionalities of the routing components to saturate the on-chip network, degrade performance, and result in the leakage of sensitive data. Existing HT detection techniques, including runtime monitoring and state-of-the-art learning-based methods, are unable to timely and accurately identify the implanted HTs, due to the increasingly dynamic and complex nature of on-chip communication behaviors. We propose AGAPE, a novel Generative Adversarial Network (GAN)-based anomaly detection and mitigation method against HTs for secured on-chip communication. AGAPE learns the distribution of the multivariate time series of a number of NoC attributes captured by on-chip sensors under both HT-free and HT-infected working conditions. The proposed GAN can learn the potential latent interactions among different runtime attributes concurrently, accurately distinguish abnormal attacked situations from normal SoC behaviors, and identify the type and location of the implanted HTs. Using the detection results, we apply the most suitable protection techniques to each type of detected HTs instead of simply isolating the entire HT-infected router, with the aim to mitigate security threats as well as reducing performance loss. Simulation results show that AGAPE enhances the HT detection accuracy by 19%, reduces network latency and power consumption by 39% and 30%, respectively, as compared to state-of-the-art security designs.

With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can share their private data with efficient search function and high-strength security. However, the search method has yet to be perfected, compared with the plaintext search mechanism. In this paper, based LSSS matrix, we give a new searchable algorithm, which is suitable for many search method, such as exact search, Boolean search and range search. In order to improve the search efficiency, the 0, 1-coding theory is introduced in the process of ciphertext search. Meanwhile it is shown that multi-search mechanism can improve the efficiency of data sharing. Finally, the performance analysis is presented, which prove our scheme is secure, efficient, and human-friendly.

Order-revealing encryption (ORE) is a basic cryptographic primitive for ciphertext comparisons based on the order relationship of plaintexts while maintaining the privacy of them. In the data era we are experiencing, cross-dataset transactions become ubiquitous in practice. However, almost all the previous ORE schemes can only support comparisons on ciphertexts from the same user, which does not meet the requirement for the multi-user environment. In this work, we introduce and design ORE schemes with delegation functionality, which is referred to as delegatable ORE (DORE). The "delegation" here is an authorization that allows for efficient ciphertext comparisons among different users. To the best of our knowledge, it is the first ORE that allows an user to delegate the comparison privilege for his ciphertexts, which also opens the door for future explorations. At the heart of the construction and analysis of DORE is a new building tool proposed in this work, named delegatable equality-revealing encoding (DERE), which might be of independent interest.