Nowadays, it is common for one natural person to join multiple social networks to enjoy different kinds of services. Linking identical users across multiple social networks, also known as social network alignment, is an important problem of great research challenges. Existing methods usually link social identities on the pairwise sample level, which may lead to undesirable performance when the number of available annotations is limited. Motivated by the isomorphism information, in this paper we consider all the identities in a social network as a whole and perform social network alignment from the distribution level. The insight is that we aim to learn a projection function to not only minimize the distance between the distributions of user identities in two social networks, but also incorporate the available annotations as the learning guidance. We propose three models SNNAu, SNNAb and SNNAo to learn the projection function under the weakly-supervised adversarial learning framework. Empirically, we evaluate the proposed models over multiple datasets, and the results demonstrate the superiority of our proposals.

Quantum key distribution (QKD) based on coherent states is well known for its implementation simplicity, but it suffers from loss-dependent attacks based on optimal unambiguous state discrimination. Crucially, previous research has suggested that coherent-state QKD is limited to short distances, typically below 100 km, assuming standard optical fiber loss and system parameters. In this work, we propose a six-coherent-state phase-encoding QKD protocol that is able to tolerate the total loss of up to 38 dB, assuming realistic system parameters, and up to 56 dB loss, assuming zero noise. The security of the protocol is calculated using a recently developed security proof technique based on semidefinite programming, which assumes only the inner-product information of the encoded coherent states, the expected statistics, and that the measurement is basis independent. Our results thus suggest that coherent-state QKD could be a promising candidate for high-speed provably secure QKD.

Self-testing, which refers to device-independent characterization of the state and the measurement, enables the security of a quantum information processing task certified independently of the operation performed inside the devices. Quantum states lie at the core of self-testing as key resources. However, for different entangled states, usually, different measurement settings should be taken in self-testing recipes. This may lead to the redundancy of measurement resources. In this work, we use fixed two-binary measurements and answer the question of which states can be self-tested with the same settings. By investigating the structure of generalized tilted-Clauser-Horne-Shimony-Holt Bell operators with the sum-of-squares decomposition method, we show that a family of two-qubit entangled states can be self-tested with the same measurement settings. The robustness analysis indicates that our scheme is feasible for practical experiment instruments. Moreover, our results can be applied to various quantum information processing tasks.

Oblivious key transfer (OKT) is a fundamental problem in the field of secure multi-party computation. It makes the provider send a secret key sequence to the user obliviously, i.e., the user may only get almost one bit key in the sequence which is unknown to the provider. Recently, a number of works have sought to establish the corresponding quantum oblivious key transfer model and rename it as quantum oblivious key distribution (QOKD) from the well-known expression of quantum key distribution (QKD). In this paper, a new QOKD model is firstly proposed for the provider and user with limited quantum capabilities, where both of them just perform computational basis measurement for single photons. Then we show that the privacy for both of them can be protected, since the probability of getting other’s raw-key bits without being detected is exponentially small. Furthermore, we give the solutions to some special decision problems such as set-member decision and point-inclusion by announcing the improved shifting strategies followed QOKD. Finally, the further discussions and applications of our ideas have been presented.