Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Abstract

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 overgeneralization 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.

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

“…A detailed experimental analysis to measure the significance of DR-AHEPP for privacy preserving of big healthcare data via quasi-identifier is presented in this section. Moreover, on the basis of the state-of-the-art methods provided in literature, user attribute susceptibility and entropy-based data anonymization (Majeed and Lee, 2020) and random k-anonymous (Song et al , 2019), the evaluation of privacy preservation of big healthcare data via quasi-identifiers is measured in terms of false positive rate, information loss and accuracy to the different unique number of patients using the Diabetes 130-US hospitals dataset. Privacy preservation experiments are analyzed with the aid of this dataset via Python.…”

“…It is measured in terms of percentage (%). The tabulation of the false positive rate for proposed DR-AHEPP and two existing methods, user attribute susceptibility and entropy-based data anonymization (Majeed and Lee, 2020) and random k-anonymous (Song et al , 2019) is reflected in Table 1. As observed from the table, the minimal false positive rate is observed from the DR-AHEPP method.…”

“…A novel user attribute susceptibility and entropy-based data anonymization that extensively enhanced the users' community privacy on anonymous data utility in publishing data was proposed (Majeed and Lee, 2020). Here, the users' community privacy was safeguarded by specifically quantifying the attribute susceptibility present in user's dataset.…”

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

“…The proposed SBC algorithm significantly reduces information loss and retains better semantics of the original dataset. Recently, the k-anonymity concept has been extended in combination with entropy concept to protect the users' groups privacy in data publishing [107]. The proposed anonymization method effectively resolves the users' groups privacy issues stemming from the low diverse ECs, and highly susceptible QIs present in a person-specific dataset.…”

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

“…Therefore, practical countermeasures to avoid such attacks are needed in the emerging technologies context. Furthermore, applying a weightage concept to attribute values in order to safeguard individual privacy is a relatively new research area [421]- [423]. Therefore, devising PPTs that can extract attribute information to the greatest extent possible in order to enable secure personal data sharing is a vibrant area of research.…”