An efficient approach for publishing microdata for multiple sensitive attributes

Anjum, Adeel; Ahmad, Naveed; Malik, Saif Ur Rehman; Zubair, Samiya; Shahzad, Basit

doi:10.1007/s11227-018-2390-x

Cited by 33 publications

(41 citation statements)

References 17 publications

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“…Lin et al [27] proposed a novel (k, p)-anonymity framework to solve the disclosure problem of sensitive attributes in the k-anonymity and l-diversity models. Anjum et al [28] proposed an efficient approach for the anonymization of multiple sensitive attributes, called (p, k)-Angelization. The (p, k)-Angelization approach not only protects the privacy of the individual, but also improves the utility of the released information.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Xiao

2020

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Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.

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Section: Related Workmentioning

confidence: 99%

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Xiao

2020

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“…e (p, k)-Angelization [22] algorithm directly adopts the single SA approach named as angelization [23] to implement privacy for MSAs. is approach invalidates the (p, k)-Angelization for the fcorr attack.…”

Section: Motivationmentioning

confidence: 99%

“…e bk includes the fact that certain pattern of values in published data is more likely to be observed than other values. For example, this knowledge can be fingerprint correlation (fcorr) knowledge, QI knowledge (qik) [10], or nonmembership knowledge (nmk) [21,22]. MSA values in a table that belongs to a specific individual form a fingerprint.…”

Section: Introductionmentioning

confidence: 99%

“…e qik is the personally identifiable information (PII) [21] for an adversary to uniquely identify an individual, and according to nmk, an individual cannot be linked to a specific sensitive value (SV). e (p, k)-Angelization [22] is a strong privacy algorithm for MSAs, where p represents the different sensitivity level of categorical SAs and k implies the k-anonymous QIs. e (p, k)-Angelization algorithm shown in Tables 2 and 3 are obtained from the original microdata in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…e (p, k)-Angelization algorithm shown in Tables 2 and 3 are obtained from the original microdata in Table 1. e authors in [22] overcame the problem of the nmk attack but still privacy could be breached with fcorr named as the fcorr attack.…”

Section: Introductionmentioning

confidence: 99%

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Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

Khan

Tao

Anjum

et al. 2020

Wireless Communications and Mobile Computing

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Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “c,k-anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.

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Dynamic distributed KC_i‐slice data publishing model with multiple sensitive attributes

Raju¹,

Naresh²

2022

Concurrency and Computation

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Privacy-preserving data publishing (PPDP) provides scope for performing various types of analytics by the researcher on the published data without revealing the individuals' privacy. However, it is necessary to consider the data from all the available locations to obtain accurate results from the published data. Then only the derived associations from the published data are helpful to make new decisions by an organization. Existing PPDP models on multiple sensitive attributes like KC-slice (K stands for bucket size, C stands for constant threshold), KC i -slice (K-bucket size, C i -variable threshold), Novel KC i -slice, and Optimal KC i -slice models are concentrated only when data is located at a single location. This research work introduces a new model called "distributed KC i -slice," which considers the data with multiple sensitive attributes from various locations to obtain accurate results. Initially, this model proposes a novel technique called a distributed algorithm to merge the different data sets into a single data set. It combines the data differently, so that it is not possible to identify any record of a specific data set from the merged data. The proposed model also uses a new bucketization algorithm named (l,m,d) * -anonymity (l stands for distinct semantic categories of the sensitive values of a sensitive attribute in a specific bucket, m stands for the number of sensitive attributes, and d stands for different sensitive categories for the sensitive values of a sensitive attribute in each sensitive bucket) for the bucketization of the tuples into the buckets. It imposes the privacy constraints only on high sensitive values of each sensitive attribute by considering the semantic and sensitive levels of sensitive values. The distributed KC i -slice model finally publishes the data in the form of multiple sensitive tables and one quasi table. This model is implemented based on attribute sensitiveness to enhance the utility and privacy levels of the published data. K E Y W O R D S anonymization, distributed data publishing, KC i -slice, multiple sensitive attributes, privacy, utility 1 INTRODUCTION Privacy-preserving data publishing (PPDP) shows a path to obtain new relations from the published data without disturbing an individual's social status. Different existing techniques are available to anonymize the personal privacy of an individual involved in the published data. All these anonymization techniques have their own merits and demerits. It is necessary to choose a suitable anonymization technique for publishing the data. 1 N. V.S. Lakshmipathi Raju and Vankamamidi S Naresh contributed equally to this work.

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An efficient approach for publishing microdata for multiple sensitive attributes

Cited by 33 publications

References 17 publications

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

Dynamic distributed KC_i‐slice data publishing model with multiple sensitive attributes

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An efficient approach for publishing microdata for multiple sensitive attributes

Cited by 33 publications

References 17 publications

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

Dynamic distributed KCi‐slice data publishing model with multiple sensitive attributes

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Product

Resources

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Dynamic distributed KC_i‐slice data publishing model with multiple sensitive attributes