Visible to the public Biblio

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Song, W., Li, X., Lou, L., Hua, Y., Zhang, Q., Huang, G., Hou, F., Zhang, X..  2018.  High-Temperature Magnetic Properties of Anisotropic SmCo7/Fe(Co) Bulk Nanocomposite Magnets. IEEE Transactions on Magnetics. 54:1–5.
High-temperature magnetic properties of the anisotropic bulk SmCo7/Fe(Co) nanocomposite magnets prepared by multistep deformation have been investigated and compared with the corresponding isotropic nanocomposites. The anisotropic SmCo7/Fe(Co) nanocomposites with a Fe(Co) fraction of 28% exhibit much higher energy products than the corresponding isotropic nanocomposites at both room and high temperatures. These magnets show a small remanence (α = -0.022%/K) and a coercivity (β = -0.25%/K) temperature coefficient which can be comparable to those of the conventional SmCo5 and Sm2Co17 high-temperature magnets. The magnetic properties of these nanocomposites at high temperatures are sensitive to the weight fractions of the Fe(Co) phase. This paper demonstrates that the anisotropic bulk SmCo7/Fe(Co) nanocomposites have better high-temperature magnetic properties than the corresponding isotropic ones.
Wang, B., Song, W., Lou, W., Hou, Y. T..  2017.  Privacy-preserving pattern matching over encrypted genetic data in cloud computing. IEEE INFOCOM 2017 - IEEE Conference on Computer Communications. :1–9.

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.