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Bo Yin

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Cryptography and Security Networking and Internet Architecture physics.app-ph Computer Vision cond-mat.mtrl-sci cs.CY Distributed, Parallel, and Cluster Computing

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preprint2026arXiv

VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection

Privacy protection has become a critical requirement in the era of ubiquitous visual data sharing, imposing higher demands on efficient and robust privacy detection algorithms. However, current robust detection models are severely hindered by the lack of comprehensive datasets. Existing privacy-oriented datasets often suffer from limited scale, coarse-grained annotations, and narrow domain coverage, failing to capture the intricate details of sensitive information in realworld environments. To bridge this gap, we present a large-scale, fine-grained Visual Privacy Dataset (VPD-100K), designed to facilitate generalized privacy detection. We establish a holistic taxonomy comprising four primary domains: Human Presence, On-Screen Personally Identifiable Information (PII), Physical Identifiers, and Location Indicators, containing 100,000 images annotated with 33 fine-grained classes and over 190,000 object instances. Statistical analysis reveals that our dataset features long-tailed distributions, small object scales, and high visual complexity. These characteristics make the dataset particularly valuable for demanding, unconstrained applications such as live streaming, where actors frequently face unintentional, realtime information leakage. Furthermore, we design an effective frequency-enhanced lightweight module consisting of frequency-domain attention fusion and adaptive spectral gating mechanism that breaks the limitations of spatial pixel intensity to better capture the subtle details of sensitive information. Extensive experiments conducted on both diverse image and streaming videos benchmarks consistently demonstrate the effectiveness of our VPD-100K dataset and the wellcurated frequency mechanism. The code and dataset are available at https://vpd-100k.github.io/.

preprint2021arXiv

Topology Aware Deep Learning for Wireless Network Optimization

Data-driven machine learning approaches have recently been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing methods do not well handle the topology information that directly impacts the network optimization results. Directly operating on simple representations, e.g., adjacency matrices, results in poor generalization performance as the learned results depend on specific ordering of the network elements in the training data. To address this issue, we propose a two-stage topology-aware machine learning framework (TALF), which trains a graph embedding unit and a deep feed-forward network (DFN) jointly. By propagating and summarizing the underlying graph topological information, TALF encodes the topology in the vector representation of the optimization instance, which is used by the later DFN to infer critical structures of an optimal or near-optimal solution. The proposed approach is evaluated on a canonical wireless network flow problem with diverse network typologies and flow deployments. In-depth study on trade-off between efficiency and effectiveness of the inference results is also conducted, and we show that our approach is better at differentiate links by saving up to 60% computation time at over 90% solution quality.

preprint2020arXiv

Bi2Te3/Si thermophotovoltaic cells converting low temperature radiation into electricity

The thermophotovoltaic cells which convert the low temperature radiation into electricity are of significance due to their potential applications in many fields. In this work, Bi2Te3/Si thermophotovoltaic cells which work under the radiation from the blackbody with the temperature of 300 K-480 K are presented. The experimental results show that the cells can output electricity even under the radiation temperature of 300 K. The band structure of Bi2Te3/Si heterojunctions and the defects in Bi2Te3 thin films lower the conversion efficiency of the cells. It is also demonstrated that the resistivity of Si and the thickness of Bi2Te3 thin films have important effects on Bi2Te3/Si thermophotovoltaic cells. Although the cells' output power is small, this work provides a possible way to utilize the low temperature radiation.

preprint2019arXiv

Bi2Te3/Sb2Te3 Heterojunction and Thermophotovoltaic Cells Absorbing the Radiation from Room-temperature Surroundings

The thermophotovoltaic cells which can convert the infrared radiation from room-temperature surroundings into electricity are of significance due to their potential applications in many fields. In this work, narrow bandgap Bi2Te3/Sb2Te3 thin film thermophotovoltaic cells were fabricated, and the formation mechanism of Bi2Te3/Sb2Te3 p-n heterojunctions was investigated. During the formation of the heterojunctions at room temperature, both electrons and holes diffuse in the same direction from n-type Bi2Te3 thin films to p-type Sb2Te3 thin films rather than conventional bi-directional diffusion. Because the strong intrinsic excitation generates a large number of intrinsic carriers which weaken the built-in electric field of the heterojunctions, their I-V curves become similar to straight lines. It is also demonstrated that Bi2Te3/Sb2Te3 thermophotovoltaic cells can output electrical power under the infrared radiation from a room-temperature heat source. This work proves that it is possible to convert the infrared radiation from dark and room-temperature surroundings into electricity through narrow bandgap thermophotovoltaic cells.

preprint2016arXiv

A Distributed Secure Outsourcing Scheme for Solving Linear Algebraic Equations in Ad Hoc Clouds

The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application application over ad hoc clouds is outsourcing computationally intensive problems to nearby cloud agents to solve in a distributed manner.

preprint2014arXiv

Secure Key Establishment for Device-to-Device Communications

With the rapid growth of smartphone and tablet users, Device-to-Device (D2D) communications have become an attractive solution for enhancing the performance of traditional cellular networks. However, relevant security issues involved in D2D communications have not been addressed yet. In this paper, we investigate the security requirements and challenges for D2D communications, and present a secure and efficient key agreement protocol, which enables two mobile devices to establish a shared secret key for D2D communications without prior knowledge. Our approach is based on the Diffie-Hellman key agreement protocol and commitment schemes. Compared to previous work, our proposed protocol introduces less communication and computation overhead. We present the design details and security analysis of the proposed protocol. We also integrate our proposed protocol into the existing Wi-Fi Direct protocol, and implement it using Android smartphones.