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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data
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Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

机译：属性易感性和基于熵的数据匿名，以提高用户社区隐私和公用事业在发布数据中

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摘要

User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users' data publishing because some attributes may expose multiple users' identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users' communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users' community can occur from the privacy preserved published data. Each item of user attributes impacts users' community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users' unique identifications and sensitive information inferences more easily, and their presence in published data increases users' community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user's community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users' community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user's dataset to effectively preserve users' community privacy. Data generalization is performed adaptively by considering both user attributes' susceptibility and entropy simultaneously. The proposed algorithm controls over-generalization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users' community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.
机译：用户属性会影响社区（即，一群人具有一些共同属性/属性的人）在用户的数据发布中隐私，因为某些属性可能会在发布的数据分析期间暴露多个用户的身份及其相关的敏感信息。用户属性（如性别，年龄和种族）可能允许基于它们的值形成用户的社区，并随后发射敏感信息推理攻击。因此，可以从保留的已发布数据的隐私泄露的特定用户社区的私人信息的明确披露。每个用户属性都会影响用户的社区隐私，而某些类型的属性是高度敏感的。更容易受到的类型属性使多个用户的唯一标识和敏感信息更容易推断，并且它们在发布的数据中的存在增加了用户的社区隐私风险。大多数现有的隐私模型忽略了易感属性对用户社区隐私的影响，主要专注于保护释放数据中的个人隐私。本文提出了一种新的数据匿名化算法，可显着提高用户的社区隐私，而不会牺牲在发布数据中的匿名数据实用程序上的保证。所提出的算法量化了用户数据集中存在的每个属性的易感性，以有效地保护用户的社区隐私。通过考虑用户属性的易感性和熵同时，自适应地执行数据泛化。该算法控制了数据的过度泛化，以增强合法信息消费者的匿名数据实用程序。由于社交网络（SNS）的广泛应用，我们专注于SN用户的社区隐私保留和实用性增强了匿名数据发布。从广泛的实验获得的模拟结果，以及与现有算法的比较显示了所提出的算法的有效性并验证上述权利要求。

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