隐私问题一直是Lifelog研究领域的热点问题之一。然而,由于目前数据集中存在隐私风险,这不但限制了研究者公开Lifelog数据集,也妨碍了研究者之间分享他们的数据集及研究成果。随着可穿戴设备和智能手机的广泛应用,Lifelog研究进入了一个新的阶段,其数据类型也变得愈发丰富,通常涵盖GPS、视频、图片、文本、语音等多种形式。针对目前多种数据格式的Lifelog数据集,我们提出了一个LPPM (Lifelog Privacy Protection Model)隐私保护模型。针对不同的数据类型,该模型可以选择不同的隐私策略。同时该模型还提出了一种基于场景的图片隐私策略SPP (Scene-Based Privacy Protection),该策略将首先预测Lifelog图片的场景,然后根据场景选取不同的隐私保护方法。我们在LiuLifelog数据集上对提出的模型进行了验证,通过LPPM模型对数据集的处理,我们认为我们的Lifelog数据集达到了可公开的程度,图片中大多数隐私被很好地掩盖了,这进一步说明我们提出的模型方法是有效的。Privacy issues have always been a hot topic in the field of Lifelog research. However, due to the current privacy risks present in datasets, researchers are not only limited in publicly sharing Lifelog datasets but also hindered in sharing their datasets and research findings among themselves. With the widespread adoption of wearable devices and smartphones, Lifelog research has entered a new stage, and the data types have become increasingly rich, typically encompassing various forms such as GPS, video, images, text, and audio. In response to the current multi-format Lifelog datasets, we propose an LPPM (Lifelog Privacy Protection Model) privacy protection model. For different data types, this model can choose different privacy strategies. Moreover, the model proposes a scene-based image privacy strategy called SPP (Scene-based Privacy Protection), which will first predict the scenes of Lifelog images and then select different privacy protection methods based on the scenes. We validated the proposed model on the LiuLifelog dataset. Through the processing of the dataset using the LPPM model, we believe our Lifelog dataset has reached a publishable level, with most privacy in the images well obscured. This further demonstrates the effectiveness of our proposed model and method.
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