35 research outputs found
Improvements on "Multi-Party Quantum Summation without a Third Party based on -Dimensional Bell States"
In 2021, Wu et al. presented a multi-party quantum summation scheme
exploiting the entanglement properties of d-dimensional Bell states (Wu et al.
in Quantum Inf Process 20:200, 2021). In particular, the authors proposed a
three-party quantum summation protocol and then extended their work to a
multi-party case. It is claimed that their protocol is secure against outside
and participants' attacks. However, this work points out that Wu's protocol has
a loophole, i.e., two or more dishonest participants who meet a specific
location relationship can conspire to obtain the private inputs of some honest
participants without being detected. Accordingly, improvements are proposed to
address these issues
Detection and evaluation of abnormal user behavior based on quantum generation adversarial network
Quantum computing holds tremendous potential for processing high-dimensional
data, capitalizing on the unique capabilities of superposition and parallelism
within quantum states. As we navigate the noisy intermediate-scale quantum
(NISQ) era, the exploration of quantum computing applications has emerged as a
compelling frontier. One area of particular interest within the realm of
cyberspace security is Behavior Detection and Evaluation (BDE). Notably, the
detection and evaluation of internal abnormal behaviors pose significant
challenges, given their infrequent occurrence or even their concealed nature
amidst vast volumes of normal data. In this paper, we introduce a novel quantum
behavior detection and evaluation algorithm (QBDE) tailored for internal user
analysis. The QBDE algorithm comprises a Quantum Generative Adversarial Network
(QGAN) in conjunction with a classical neural network for detection and
evaluation tasks. The QGAN is built upon a hybrid architecture, encompassing a
Quantum Generator () and a Classical Discriminator (). ,
designed as a parameterized quantum circuit (PQC), collaborates with , a
classical neural network, to collectively enhance the analysis process. To
address the challenge of imbalanced positive and negative samples, is
employed to generate negative samples. Both and are optimized
through gradient descent techniques. Through extensive simulation tests and
quantitative analyses, we substantiate the effectiveness of the QBDE algorithm
in detecting and evaluating internal user abnormal behaviors. Our work not only
introduces a novel approach to abnormal behavior detection and evaluation but
also pioneers a new application scenario for quantum algorithms. This paradigm
shift underscores the promising prospects of quantum computing in tackling
complex cybersecurity challenges
Multi-Party Quantum Summation Based on Quantum Teleportation
We present a secure multi-party quantum summation protocol based on quantum teleportation, in which a malicious, but non-collusive, third party (TP) helps compute the summation. In our protocol, TP is in charge of entanglement distribution and Bell states are shared between participants. Users encode the qubits in their hand according to their private bits and perform Bell-state measurements. After obtaining participants’ measurement results, TP can figure out the summation. The participants do not need to send their encoded states to others, and the protocol is therefore congenitally free from Trojan horse attacks. In addition, our protocol can be made secure against loss errors, because the entanglement distribution occurs only once at the beginning of our protocol. We show that our protocol is secure against attacks by the participants as well as the outsiders