τ -safety: A privacy model for sequential publication with arbitrary updates

Anjum, Adeel; Raschia, Guillaume; Gelgon, Marc; Khan, Abid; Malik, Saif Ur Rehman; Ahmad, Naveed; Ahmed, Mansoor; Suhail, Sabah; Alam, Mansaf

doi:10.1016/j.cose.2016.12.014

Cited by 30 publications

(83 citation statements)

References 10 publications

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“…The proposed approach jointly uses the m-invariance and counterfeited generalization concepts to solve the PPDDP problem. Anjum et al [111] proposed a τ -safety privacy model for the PPDDP. The proposed approach performs better in the presence of external and internal updates.…”

Section: F Privacy Preserving Dynamic Data Publicationmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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Section: F Privacy Preserving Dynamic Data Publicationmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

View full text Add to dashboard Cite

“…In multiple data publishing, at the same time and on the same data, different set of attributes are published [14], [36]. In sequential data publishing, many releases of the same table are published over a period of time [10][11][12], [15][16], [37][38][39]. The focus of this paper is sequential data publishing, where data are published over time having the same schema.…”

Section: Related Workmentioning

confidence: 99%

“…The background knowledge (BK); logical or probabilistic, is the information an adversary collects from different sources and personal observations that may cause a privacy breach in static [7][8][9], [21][22] or in re-publication of data [11], [16], [32], [44]. In the proposed privacy model, it is assumed that an adversary recursively updates the BK.…”

Section: Adversarial Background Knowledgementioning

confidence: 99%

“…The adversary performs membership attack if he can map the known QI attribute of an individual i to an EC, to identify the A S value with the help of available PK sv and UBK sv . Definition 3: External Update [11][12]. ∀t, the operation is said to be external update of t, if t ∈ T n or t ∈ T d .…”

mentioning

confidence: 99%

“…∀t, the operation is said to be external update of t, if t ∈ T n or t ∈ T d . Definition 4: Internal Updates [11][12]. ∀t, the operation is said to be internal update of t, if t ∈ T u or t ∈ T r .…”

mentioning

confidence: 99%

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(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility

Khan

Tao

Anjum

et al. 2020

Trans Emerging Tel Tech

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In a real-world scenario for privacy-preserving data publishing, the original data is anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine i.e. correlate different releases to compromise the privacy of the individual records. Most of the literature, such as -safety, -safe (l, k)-diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model ( , )-slicedBucket, having a novel idea of "Cache" table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of -safe (l, k)-diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed ( , )-slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.

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A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi‐records datasets

Kanwal

Anjum

Khan

et al. 2020

Trans Emerging Tel Tech

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The collection of private health data without compromising privacy is an imperative aspect of privacy‐aware data collection mechanisms. Privacy‐preserved data collection is achieved by anonymizing private data before its transmission from data holders to data collectors. Though there exist ample literature on private data collection for 1:1 (single record of a data holder) datasets, collecting multi‐records (multiple records of a data holder) datasets (referred to as 1:M datasets) has not received due attention from the research community. Therefore, the current studies experience serious privacy breaches in 1:M dataset thereby limiting their application in secure healthcare applications and systems. In this work, we have formally classified main privacy disclosures on these data collection mechanisms and proposed an improved privacy scheme, namely, horizontal sliced permuted permutation (H‐SPP) for 1:M datasets. It uses the composite slicing and anatomy‐based approach to protect against the privacy violations like identity, attribute, and membership disclosures. Moreover, we perform formal modeling of the proposed scheme using high‐level Petri nets (HLPN) and show that it effectively prevents the identified external and internal privacy attacks. Experimental results show that H‐SPP provides robust privacy for health data with high performance.

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τ -safety: A privacy model for sequential publication with arbitrary updates

Cited by 30 publications

References 10 publications

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility

A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi‐records datasets

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