Technological advancements have significantly transformed communication patterns, introducing a diverse array of online platforms, thereby prompting individuals to use multiple profiles for different domains and objectives. Enhancing the understanding of cross domain identity matching capabilities is essential, not only for practical applications such as commercial strategies and cybersecurity measures, but also for theoretical insights into the privacy implications of data disclosure. In this study, we demonstrate that individual temporal data, in the form of inter-event times distribution, constitutes an individual temporal fingerprint, allowing for matching profiles across different domains back to their associated real-world entity. We evaluate our methodology on encrypted digital trading platforms within the Ethereum Blockchain and present impressing results in matching identities across these privacy-preserving domains, while outperforming previously suggested models. Our findings indicate that simply knowing when an individual is active, even if information about who they talk to and what they discuss is lacking, poses risks to users' privacy, highlighting the inherent challenges in preserving privacy in today's digital landscape.

翻译：技术进步已显著改变了通信模式，引入了多样化的在线平台，从而促使个人为不同领域和目标使用多个身份档案。增强对跨领域身份匹配能力的理解至关重要，这不仅对商业策略和网络安全措施等实际应用具有重要意义，也为理解数据披露的隐私影响提供了理论洞见。在本研究中，我们证明个体时间数据——以事件间隔时间分布的形式——构成了一种个体时间指纹，使得跨不同领域的身份档案能够匹配回其关联的现实世界实体。我们在以太坊区块链内的加密数字交易平台上评估了我们的方法，并在这些隐私保护领域中展示了令人印象深刻的身匹配结果，同时超越了先前提出的模型。我们的研究结果表明，仅知晓个体的活动时间（即使缺乏关于其交流对象和讨论内容的信息）也会对用户隐私构成风险，这突显了在当今数字环境中保护隐私所面临的内在挑战。