Fenghao Dong (CMU)

Network packet traces are critical for security tasks which includes longitudinal traffic analysis, system testing, and future workload forecasting. However, storing these traces over extended periods is costly and subject to compliance constraints. Deep Generative Compression (DGC) offers a solution by generating inexact but structurally accurate synthetic traces that preserve essential features without storing full sensitive data. This paper examines key research questions on the feasibility, cost-competitiveness, and scalability of DGC for large-scale, real-world network environments. We investigate the types of applications that benefit from DGC and design a framework to reliably operate for them. Our initial evaluation indicates that DGC can be an alternative to standard storage techniques (such as gzip or sampling) while meeting regulatory needs and resource limits. We further discuss open challenges and future directions, such as improving efficiency in streaming operations, optimizing model scalability, and addressing privacy risks in this scenario.

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Diffence: Fencing Membership Privacy With Diffusion Models

Yuefeng Peng (University of Massachusetts Amherst), Ali Naseh (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

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Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

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Victim-Centred Abuse Investigations and Defenses for Social Media Platforms

Zaid Hakami (Florida International University and Jazan University), Ashfaq Ali Shafin (Florida International University), Peter J. Clarke (Florida International University), Niki Pissinou (Florida International University), and Bogdan Carbunar (Florida International University)

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Towards LLM-Assisted Vulnerability Detection and Repair for Open-Source 5G...

Rupam Patir (University at Buffalo), Qiqing Huang (University at Buffalo), Keyan Guo (University at Buffalo), Wanda Guo (Texas A&M University), Guofei Gu (Texas A&M University), Haipeng Cai (University at Buffalo), Hongxin Hu (University at Buffalo)

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