Secure Sensitive Data Sharing on a Big Data Platform

Users store vast amounts of sensitive data on a big data platform. Sharing sensitive data will help enterprises reduce the cost of providing users with personalized services and provide value-added data services.However, secure data sharing is problematic. This paper proposes a framework for secure sensitive data sharing on a big data platform, including secure data delivery, storage, usage, and destruction on a semi-trusted big data sharing platform. We present a proxy re-encryption algorithm based on heterogeneous ciphertext transformation and a user process protection method based on a virtual machine monitor, which provides support for the realization of system functions. The framework protects the security of users＇ sensitive data effectively and shares these data safely. At the same time, data owners retain complete control of their own data in a sound environment for modern Internet information security.

Fast holonomic quantum computation based on solid-state spins with all-optical control

Holonomic quantum computation is a quantum computation strategy that promises some built-in noise-resilience features. Here,we propose a scheme for nonadiabatic holonomic quantum computation with nitrogen-vacancy center electron spins, which are characterized by fast quantum gates and long qubit coherence times. By varying the detuning, amplitudes, and phase difference of lasers applied to a nitrogen-vacancy center, one can directly realize an arbitrary single-qubit holonomic gate on the spin.Meanwhile, with the help of cavity-assisted interactions, a nontrivial two-qubit holonomic quantum gate can also be induced. The distinct merit of this scheme is that all the quantum gates are obtained via an all-optical geometric manipulation of the solid-state spins. Therefore, our scheme opens the possibility for robust quantum computation using solid-state spins in an all-optical way.