Pairing Users in Social Media via Processing Meta-data from Conversational Files
Pairing Users in Social Media via Processing Meta-data from Conversational Files
复制标题
通过处理会话文件中的元数据在社交媒体中配对用户
DOI：
10.1007/978-3-030-37188-3_7
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Chaudhary, Meghana
Sharma
中科院分区：
文献类型：
--
作者：
Chaudhary, Meghana
Sharma
如今，社交媒体上的用户产生了大量数据。尽管知道自己在社交媒体上发布的任何内容都可能被未经授权的实体查看、下载和分析，但今天仍有很多人愿意牺牲自己的隐私。但另一方面，这种趋势可能会改变。保护社交媒体内容意识的提高，加上政府制定和执行数据保护法，意味着在不久的将来，用户可能会越来越保护他们分享的内容。此外，新的法律可能会限制社交媒体公司在未经用户明确同意的情况下可以使用的数据。在本文中，我们提出并解决了社交媒体日志中隐私保护挖掘的一个相对较新的问题。具体来说，这里的问题是推导网络通信拓扑的可行性（即，匹配社交网络中的发送者和接收者），但在匿名化所有身份和内容之后，仅使用用户共享的会话文件的元数据。更明确地说，如果用户只愿意共享(a)消息是否被发送或接收，(b)消息的时间顺序和(c)每条消息的长度（在匿名化其他所有内容之后，包括来自社交媒体日志的用户名），如何生成发送方-接收方模式的底层拓扑。为了解决这个问题，我们提出了一个基于动态时间翘曲的解决方案，将元数据建模为时间序列序列。我们提出了一个形式化的算法和有趣的结果，在多个场景中，用户可能会或可能不会在分享之前随意删除内容。我们的性能结果在Twitter上下文中应用时非常有利。在论文的最后，我们还介绍了我们的问题和解决方案的有趣的实际应用。据我们所知，我们解决的问题和我们提出的解决方案是独一无二的，并且可以为从社交媒体日志中学习隐私保护提供重要的未来视角。
Massive amounts of data today are being generated from users engaging on social media. Despite knowing that whatever they post on social media can be viewed, downloaded and analyzed by unauthorized entities, a large number of people are still willing to compromise their privacy today. On the other hand though, this trend may change. Improved awareness on protecting content on social media, coupled with governments creating and enforcing data protection laws, mean that in the near future, users may become increasingly protective of what they share. Furthermore, new laws could limit what data social media companies can use without explicit consent from users. In this paper, we present and address a relatively new problem in privacy-preserved mining of social media logs. Specifically, the problem here is the feasibility of deriving the topology of network communications (i.e., match senders and receivers in a social network), but with only meta-data of conversational files that are shared by users, after anonymizing all identities and content. More explicitly, if users are willing to share only (a) whether a message was sent or received, (b) the temporal ordering of messages and (c) the length of each message (after anonymizing everything else, including usernames from their social media logs), how can the underlying topology of sender-receiver patterns be generated. To address this problem, we present a Dynamic Time Warping based solution that models the meta-data as a time series sequence. We present a formal algorithm and interesting results in multiple scenarios wherein users may or may not delete content arbitrarily before sharing. Our performance results are very favorable when applied in the context of Twitter. Towards the end of the paper, we also present interesting practical applications of our problem and solutions. To the best of our knowledge, the problem we address and the solution we propose are unique, and could provide important future perspectives on learning from privacy-preserving mining of social media logs.
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1145/3133956.3134077
发表时间：
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期刊：
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DOI：
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影响因子：
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DOI：
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发表时间：
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期刊：
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