Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection

• URL: http://arxiv.org/abs/2407.18274v1
• Date: Tue, 23 Jul 2024 11:19:22 GMT
• Title: Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection
• Authors: Zhiwei Yang, Yuecen Wei, Haoran Li, Qian Li, Lei Jiang, Li Sun, Xiaoyan Yu, Chunming Hu, Hao Peng,
• Abstract summary: Social event detection is important in numerous areas, such as opinion analysis, social safety, and decision-making. Most current methods are supervised and require access to large amounts of data. We propose ADP-SEMEvent, an unsupervised social event detection method that prioritizes privacy.
• Score: 29.13690542566747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social event detection refers to extracting relevant message clusters from social media data streams to represent specific events in the real world. Social event detection is important in numerous areas, such as opinion analysis, social safety, and decision-making. Most current methods are supervised and require access to large amounts of data. These methods need prior knowledge of the events and carry a high risk of leaking sensitive information in the messages, making them less applicable in open-world settings. Therefore, conducting unsupervised detection while fully utilizing the rich information in the messages and protecting data privacy remains a significant challenge. To this end, we propose a novel social event detection framework, ADP-SEMEvent, an unsupervised social event detection method that prioritizes privacy. Specifically, ADP-SEMEvent is divided into two stages, i.e., the construction stage of the private message graph and the clustering stage of the private message graph. In the first stage, an adaptive differential privacy approach is used to construct a private message graph. In this process, our method can adaptively apply differential privacy based on the events occurring each day in an open environment to maximize the use of the privacy budget. In the second stage, to address the reduction in data utility caused by noise, a novel 2-dimensional structural entropy minimization algorithm based on optimal subgraphs is used to detect events in the message graph. The highlight of this process is unsupervised and does not compromise differential privacy. Extensive experiments on two public datasets demonstrate that ADP-SEMEvent can achieve detection performance comparable to state-of-the-art methods while maintaining reasonable privacy budget parameters.

Related papers

• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Masked Differential Privacy [64.32494202656801]
  We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied. Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Double Mixture: Towards Continual Event Detection from Speech [60.33088725100812]
  Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. This paper tackles two primary challenges in speech event detection: the continual integration of new events without forgetting previous ones, and the disentanglement of semantic from acoustic events. We propose a novel method, 'Double Mixture,' which merges speech expertise with robust memory mechanisms to enhance adaptability and prevent forgetting.
  arXiv  Detail & Related papers  (2024-04-20T06:32:00Z)
• Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection [61.87480191351659]
  Graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs.
  arXiv  Detail & Related papers  (2023-12-19T06:28:32Z)
• A Summary of Privacy-Preserving Data Publishing in the Local Setting [0.6749750044497732]
  Statistical Disclosure Control aims to minimize the risk of exposing confidential information by de-identifying it. We outline the current privacy-preserving techniques employed in microdata de-identification, delve into privacy measures tailored for various disclosure scenarios, and assess metrics for information loss and predictive performance.
  arXiv  Detail & Related papers  (2023-12-19T04:23:23Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
  We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
  arXiv  Detail & Related papers  (2022-08-24T17:52:43Z)
• Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements [0.0]
  We develop an unsupervised event detection method based on the concept of Generative Adversarial Networks (GAN) We also propose a two-step unsupervised clustering method, based on a novel linear mixed integer programming formulation. Results show that they can outperform the prevalent methods in the literature.
  arXiv  Detail & Related papers  (2020-07-30T05:20:29Z)
• Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media [1.7205106391379026]
  The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection. The obtained results show that Embed2Detect is capable of effective and efficient event detection.
  arXiv  Detail & Related papers  (2020-06-10T15:52:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.