Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation

Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poisoning attack from the user side to inject carefully-crafted bogus data into the LDP protocols in order to maximally skew the final estimate by the data curator. In this work, we further advance this knowledge by proposing a new fine-grained attack, which allows the attacker to fine-tune and simultaneously manipulate mean and variance estimations that are popular analytical tasks for many real-world applications. To accomplish this goal, the attack leverages the characteristics of LDP to inject fake data into the output domain of the local LDP instance. We call our attack the output poisoning attack (OPA). We observe a security-privacy consistency where a small privacy loss enhances the security of LDP, which contradicts the known security-privacy trade-off from prior work. We further study the consistency and reveal a more holistic view of the threat landscape of data poisoning attacks on LDP. We comprehensively evaluate our attack against a baseline attack that intuitively provides false input to LDP. The experimental results show that OPA outperforms the baseline on three real-world datasets. We also propose a novel defense method that can recover the result accuracy from polluted data collection and offer insight into the secure LDP design

A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives

Graph-related applications have experienced significant growth in academia and industry, driven by the powerful representation capabilities of graph. However, efficiently executing these applications faces various challenges, such as load imbalance, random memory access, etc. To address these challenges, researchers have proposed various acceleration systems, including software frameworks and hardware accelerators, all of which incorporate graph pre-processing (GPP). GPP serves as a preparatory step before the formal execution of applications, involving techniques such as sampling, reorder, etc. However, GPP execution often remains overlooked, as the primary focus is directed towards enhancing graph applications themselves. This oversight is concerning, especially considering the explosive growth of real-world graph data, where GPP becomes essential and even dominates system running overhead. Furthermore, GPP methods exhibit significant variations across devices and applications due to high customization. Unfortunately, no comprehensive work systematically summarizes GPP. To address this gap and foster a better understanding of GPP, we present a comprehensive survey dedicated to this area. We propose a double-level taxonomy of GPP, considering both algorithmic and hardware perspectives. Through listing relavent works, we illustrate our taxonomy and conduct a thorough analysis and summary of diverse GPP techniques. Lastly, we discuss challenges in GPP and potential future directions

Research on gas production law of free gas in oil-immersed power transformer under discharge fault of different severity

Dissolved gas analysis (DGA) is a common technology used in the on-site maintenance of oil-immersed power transformers in the power industry at present. However, when the content of dissolved gas in the oil reaches the attention value DGA method can effectively diagnose the operating state of the transformer. Due to the lack of gas production data of free gas which was detected when the faults occur, DGA method cannot timely diagnose the transformer status. To solve the above problem, an experimental platform is built for studying the free gas generation law in oil-immersed transformers under discharge faults, and the characteristic free gas information under discharge fault of transformer is obtained through the experiment. It is found that the existing DGA method cannot accurately analyze the types and severity of sudden serious insulation faults. When high-energy partial discharge fault occurred in the equipment, CO, CO2, CH4, and H2 will be collected in large quantities on the oil surface. These four gases can be used as the basis for characterizing high-energy partial discharge faults. When spark discharge occurred in the equipment, C2H6, C2H4, and C2H2 also be collected on the oil surface which can be used as a diagnostic basis for spark discharge. Moreover, it is found that the existing three-ratio method cannot be used for accurate analysis of oil free characteristic gas, so it is necessary to explore new diagnostic methods. The aim of this study is to explore the pattern of free gas production law by experiments when discharge faults occur and to provide data for a new diagnostic method