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首页> 外文会议>2018 International Conference on Computing, Mathematics and Engineering Technologies >Anatomization through generalization (AG): A hybrid privacy-preserving approach to prevent membership, identity and semantic similarity disclosure attacks
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Anatomization through generalization (AG): A hybrid privacy-preserving approach to prevent membership, identity and semantic similarity disclosure attacks

机译：概括化解剖（AG）：一种混合的隐私保护方法，可防止成员资格，身份和语义相似性披露攻击

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摘要

Individuals' data is creating a new trend of opportunity for different organizations. This data is termed as a tradable asset for business. Most of the companies collect and store data of individuals to be used for direct activities such as providing better services to their customers, or to be released for non-direct activities such as analysis, doing research, marketing and public health. This collected data may include sensitive information like criminal records, financial records and medical records, which may result in privacy threats if compromised. A number of approaches are used to ensure Privacy-Preserving Data Publishing (PPDP). But most of the existing methods don't prevent all main privacy disclosure attacks or cause substantial loss of information. In order to prevent membership, identity and semantic similarity attacks while maintaining usefulness of data, a hybrid approach is proposed in this paper. This approach combines the bucketization method of anatomization approach and generalization as well as suppression methods of anonymization approach to achieve the two major privacy requirements: (l, e) diversity and k-anonymity. Our experiment shows that from the view of data privacy, the proposed technique increases the diversity degree of sensitive values by 29% and 37% on average over (l, e) diversity and klredInfo techniques respectively. On the other hand from the view of information loss, the proposed technique reduces the Discernibility Penalty (DP)D by 30% on average over (l, e) diversity technique and increases it by 28% on average over klredIinfo technique. In addition, the proposed technique increased the Normalized Certainty Penalty (NCP) by 12% on average over klredInf technique. Hence the proposed technique preserves data privacy more effectively as compared to klredInfo and (l, e) diversity techniques while maintaining the utility of data.
机译：个人数据正在为不同组织创造新的机会趋势。此数据被称为业务的可交易资产。大多数公司收集和存储个人数据，以用于直接活动，例如为客户提供更好的服务，或将其发布用于非直接活动，例如分析，进行研究，营销和公共卫生。这些收集的数据可能包括敏感信息，例如犯罪记录，财务记录和医疗记录，如果受到威胁，可能会导致隐私威胁。许多方法用于确保隐私保护数据发布（PPDP）。但是，大多数现有方法无法阻止所有主要的隐私披露攻击或造成大量信息丢失。为了防止成员资格，身份和语义相似性攻击，同时保持数据的有用性，本文提出了一种混合方法。这种方法结合了解剖方法和泛化的桶化方法以及匿名方法的抑制方法，以实现两个主要的隐私要求：（l，e）分集和k-匿名性。我们的实验表明，从数据隐私的角度来看，与（l，e）分集和klredInfo技术相比，该技术将敏感值的分集度平均提高了29％和37％。另一方面，从信息丢失的角度来看，所提出的技术与（l，e）分集技术相比，平均降低了区分罚分（DP）D 30％，与klredIinfo技术相比，平均提高了28％。此外，与klredInf技术相比，提出的技术平均将标准化确定性惩罚（NCP）提高了12％。因此，与klredInfo和（l，e）分集技术相比，所提出的技术在保持数据效用的同时更有效地保护了数据隐私。

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