Gate-Tunable Tunneling Resistance in Graphene/Topological Insulator Vertical Junctions

Graphene-based vertical heterostructures, particularly stacks incorporated with other layered materials, are promising for nanoelectronics. The stacking of two model Dirac materials, graphene and topological insulator, can considerably enlarge the family of van der Waals heterostructures. Despite well understanding of the two individual materials, the electron transport properties of a combined vertical heterojunction are still unknown. Here we show the experimental realization of a vertical heterojunction between Bi2Se3 nanoplate and monolayer graphene. At low temperatures, the electron transport through the vertical heterojunction is dominated by the tunneling process, which can be effectively tuned by gate voltage to alter the density of states near the Fermi surface. In the presence of a magnetic field, quantum oscillations are observed due to the quantized Landau levels in both graphene and the two-dimensional surface states of Bi2Se3. Furthermore, we observe an exotic gate-tunable tunneling resistance under high magnetic field, which displays resistance maxima when the underlying graphene becomes a quantum Hall insulator

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page