INFOCOM 2025 Papers

Role discovery assist in various applications of online social networks, such as water army detection, shopping recommendation, rumor tracing, etc. However, existing studies often overlook the significance of hierarchical structures in online social networks, which are crucial for understanding the roles played by different users. To address this gap, we propose a novel approach based on hyperbolic graph learning, called HyperRole, which effectively leverages the hierarchical structure of online social networks for role discovery. HyperRole first extracts structural features from users and constructs user sequences based on feature similarity, capturing the relationships between users across different scales. Then, we learn role information from structural features by hyperbolic graph Transformer to embed users into the hyperbolic space, preserving the hierarchical structure between users and enabling interactions between users of the same level that are far away from each other. Additionally, we leverage the hierarchical distance between the target user and other users within the same sequence to guide and modify the role information of the target user. Based on the generated user role embeddings, we train a multi-class classifier to classify roles. Extensive experiments on several real-world network datasets demonstrate that our model outperforms existing baseline methods, showcasing its superior performance.

Tor, a widely utilized privacy network, enables anonymous communication but is vulnerable to flow correlation attacks that deanonymize users by correlating traffic patterns from Tor's ingress and egress segments. Various defenses have been developed to mitigate these attacks; however, they have two critical limitations: (i) significant network overhead during obfuscation and (ii) a lack of dynamic obfuscation for egress segments, exposing traffic patterns to adversaries. In response, we introduce MUFFLER, a novel connection-level traffic obfuscation system designed to secure Tor egress traffic. It dynamically maps real connections to a distinct set of virtual connections between the final Tor nodes and targeted services, either public or hidden. This approach creates egress traffic patterns fundamentally different from those at ingress segments without adding intentional padding bytes or timing delays. The mapping of real and virtual connections is adjusted in real-time based on ongoing network conditions, thwarting adversaries' efforts to detect egress traffic patterns. Extensive evaluations show that MUFFLER mitigates powerful correlation attacks with a TPR of 1% at an FPR of 10−2while imposing only a 2.17% bandwidth overhead. Moreover, it achieves up to 27x lower latency overhead than existing solutions and seamlessly integrates with the current Tor architecture.

Providing persuasive descriptions of points of interest (POI) is crucial for ensuring the quality of location-based services such as food delivery. However, informal textual descriptions by customers and unreliable geographic coordinates from indoor devices make it challenging to standardize both textual and geospatial descriptions of queried POIs. Previous works often take a retrieve-then-rank approach, which has limited feasibility in real-world delivery scenarios due to their dependency on a vast POI database and the extensive labor required for labeling. In this work, we propose the AMoS system, which is based on the observation that records referring to the same POI are either similar in both semantic and geospatial domains, or at least in one of them. The AMoS system leverages inherent semantic and geospatial similarities within historical POI records, combining them in graph-based clustering to retrieve candidates for standardizing a given query. We also propose a standardization paradigm that combines structured formatting rules of POI descriptions with content diversity. We evaluate the performance on 22356 orders from a real-world online delivery platform. Our retrieval performance outperforms baselines by 16% in precision and remains competitive compared to a supervised approach. Our standardization accuracy reaches 90.24%.

Advances in deep neural networks (DNNs) have significantly contributed to the development of real-time video processing applications. Efficient scheduling of DNN workloads in cloud-hosted inference systems is crucial to minimizing serving costs while meeting application latency constraints. However, existing systems suffer from excessive module latency during request dispatching, low execution throughput during module scheduling, and wasted latency budget during latency splitting for multi-DNN applications, which undermines their capability to minimize the serving cost. In this paper, we design a DNN inference system called Harpagon, which minimizes the serving cost under latency constraints with a three-level design. It first maximizes the batch collection rate with a batch-aware request dispatch policy to minimize the module latency. It then maximizes the module throughput with multi-tuple configurations and proper amount of dummy requests. It also carefully splits the end-to-end latency into per-module latency budget to minimize the total serving cost for multi-DNN applications. Evaluation shows that Harpagon outperforms the state of the art by 1.49 to 2.37 times in serving cost while satisfying the latency objectives. Additionally, compared to the optimal solution using brute force search, Harpagon derives the lower bound of serving cost for 91.5% workloads with millisecond level runtime.