A Privacy-Preserving Data Publishing Method for Multiple Numerical Sensitive Attributes via Clustering and Multi-sensitive Bucketization

Nowadays Privacy Preserving in Data Publishing (PPDP) has rapidly growth into the research contents in data protection field and also it has rapidly grown in publication of personal data. Recent years in data publishing is exceptionally analytical, because, how to economically preserve numerical and categorical sensitive attribute. However, different tender have been designed for privacy preserving to protect numerical sensitive attribute in data publishing, Like k-anonymity, l-diversity, t-closeness, (epsilon, m)-anonymity another methods are implemented for protecting the privacy of data provider. In this paper, we propose a review of various multiple numerical and categorical sensitive attribute for preserving privacy and survey current existing techniques. Yet, we discuss the future instructions of privacy preserving in data publishing, that is we suggest modern technique for “A new enhanced slicing method for both numerical and categorical sensitive attribute via advanced clustering algorithm” that improving the privacy with less information loss, membership, attribute , identity disclosure and error ratio and also calculate distance between two attributes for categorical attribute. This technique suitable for both categorical and numerical sensitive attributes.

In today’s world, information is a hot topic to discuss and explore. We all strive to get more information and utilize it for advancement. Data is the main object to get information. Data is considered as a big data nowadays as it contains a massive amount of information. Knowing the benefits of knowledge does not mean an adversary cannot use the same information to harm an individual. Protecting data from the intrusion of an adversary can be done through Privacy Preserving Data Publishing Methods. When there is a combination of sensitive data in a data set that is correlated to each other, several models are not efficient for protecting data. We propose a novel model K-MNSSOA for preserving data privacy based on the concept of K-anonymity in this paper. An in-depth study of the K-MNSOA model for Privacy Preserving Data Publishing is presented in this article, which protects sensitive data privacy breaches even when the set of data has multiple overlapping sensitive numerical attributes. Using this model, levels of sensitivity are designed, and generalization is performed on overlapping attributes based on the sensitivity in sensitive attributes.KeywordsData hidingBig dataPrivacy preservation techniquesK-AnonymityPrivacy breach attack

Data collected at the organizations such as schools, offices, healthcare centers and e-commerce websites contain multiple sensitive attributes. The sensitive information from these organisations such as marks obtained, salary, disease, treatment and traveling history are personal information that an individual dislikes to disclose to the public as it may lead to privacy threats. Therefore, it is necessary to preserve privacy of the data before publishing. Privacy Preserving Data Publishing(PPDP) algorithms aim to publish the data without compromising the privacy of individuals. In the recent years several algorithms have been designed for PPDP multiple sensitive attributes. The major limitations are, firstly among several sensitive attributes these algorithms consider one of them as primary sensitive attribute and anonymize the data, however there may be other dominant sensitive attributes that need to be preserved. Secondly, there is no consistent way to categorize multiple sensitive attributes. Lastly, increased proportion of records are generated due to usage of generalization and suppression techniques. Hence, to overcome these limitations the current work proposes an efficient approach to categorize the sensitive attributes based their semantics and anonymize the data using an anatomy technique. This reduces the residual records as well as categorizes the attributes. The results are compared with popular techniques like Simple Distribution of Sensitive Values (SDSV) and (l, e) diversity. Experiments prove that our method outperforms the existing methods in terms of categorization of multiple sensitive attributes, reducing the percentage of residual records and preventing the existing privacy threats.

Privacy Preserving Publication has become one of the most prominent research topics in the recent years. Several techniques like kanonymity, l-diversity and (α, k) anonymity were proposed to preserve privacy. Most of the published work focuses on anonymizing the microdata for preserving privacy and now the focus towards the verification of the anonymity levels of the microdata before publishing is the need of the day. Many publishers claim having anonymized the data. Verification of the claim on a large anonymized dataset is a herculean task. This paper focuses on providing simple approach for checking the anonymity levels for an anonymized dataset using frequent itemset generation. A GUI based tool named PRUDENT was developed to demonstrate the practicality of the solution. PRUDENT deals with numerical, categorical and multiple sensitive attributes. Results show that the algorithm is feasible and practical. A comparison with the existing methods is shown.

Privacy preserving data publishing is a growing research in computer science. Many of study on this research focus on single sensitive attribute. While many of multiple sensitive attributes researches do not set the sensitive attribute distribution. It is necessary for ensuring p-sensitive property since multiple sensitive attributes not just apply the model in single sensitive attribute. It needs more setting. This research discusses a distribution model to set sensitive attribute values when p-sensitive is applied on multiple sensitive attributes. We build a rule to distribute sensitive values. The result shows that this distribution model satisfies privacy guarantee that is provided by p-sensitive.

To make various statistics about any medical data huge amount of data gets collected from the public. The collected information contains sensitive information about the individual. Individual's sensitive information includes data such as previous health condition, travel history, political affinity and family background. The collected data is published to perform various statistics, carry out research and to make decisions. However, there might be an intruder in the crowd who uses the published data to gain more information about a particular person that leads to privacy threat. Privacy Preserving Data Publishing (PPDP) aims at anonymizing such data so that it becomes difficult for an intruder to infer a various attribute of particular person from the published data and to gain more information about a him. When the data is anonymized, there are chances of data loss, hence the anonymization algorithm, must balance between the anonymity and the utility of the published data. Most of the existing privacy algorithm concentrate on preserving privacy of single sensitive attributes, but many domains require anonymizing multiple sensitive attributes such as healthcare/ hospital dataset, census dataset etc., that need to be preserved. In this paper an effective algorithm is proposed that preserves the privacy of multiple sensitive attributes with minimal information loss.

The publication of microdata is pivotal for medical research purposes, data analysis and data mining. These published data contain a substantial amount of sensitive information, for example, a hospital may publish many sensitive attributes such as diseases, treatments and symptoms. The release of multiple sensitive attributes is not desirable because it puts the privacy of individuals at risk. The main vulnerability of such approach while releasing data is that if an adversary is successful in identifying a single sensitive attribute, then other sensitive attributes can be identified by co-relation. A whole variety of techniques such as SLOMS, SLAMSA and others already exist for the anonymization of multiple sensitive attributes; however, these techniques have their drawbacks when it comes to preserving privacy and ensuring data utility. The extant framework lacks in terms of preserving privacy for multiple sensitive attributes and ensuring data utility. We propose an efficient approach (p, k)-Angelization for the anonymization of multiple sensitive attributes. Our proposed approach protects the privacy of the individuals and yields promising results compared with currently used techniques in terms of utility. The (p, k)-Angelization approach not only preserves the privacy by eliminating the threat of background join and non-membership attacks but also reduces the information loss thus improving the utility of the released information.

Privacy is a serious concern when microdata need to be released for ad hoc analyses. The privacy goals of existing privacy protection approaches (e.g., k-anonymity and l-diversity) are suitable only for categorical sensitive attributes. Since applying them directly to numerical sensitive attributes (e.g., salary) may result in undesirable information leakage, we propose privacy goals to better capture the need of privacy protection for numerical sensitive attributes. Complementing the desire for privacy is the need to support ad hoc aggregate analyses over microdata. Existing generalization-based anonymization approaches cannot answer aggregate queries with reasonable accuracy. We present a general framework of permutation-based anonymization to support accurate answering of aggregate queries and show that, for the same grouping, permutation-based techniques can always answer aggregate queries more accurately than generalization-based approaches. We further propose several criteria to optimize permutations for accurate answering of aggregate queries, and develop efficient algorithms for each criterion.

Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes

This paper aims to determine the main sensitive attribute in anatomy with multiple sensitive attributes. Privacy in published data becomes important and interesting research recently. Many works have been conducted on keeping published data privately. K-anonymity was proposed as the first model in this study. Yet, this model still has some drawbacks. A better model called anatomy is proposed to fix k-anonymity. Many studies on anatomy are performed on a single sensitive attribute, while in the real world, a microdata table always contains multiple sensitive attributes. This work handles problems on how to publish data in private mode by rearranging the anatomy model. We adopted the method in determining main sensitive attribute. We conducted it in the preliminary phase and its result is a prepared model of the main sensitive attribute for anatomy. The result success in ensuring one sensitive attribute as a main sensitive attribute.

Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones.

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes (SAs) have not considered the personalised privacy requirement. Furthermore, sensitive information disclosure may also be caused by these personalised requirements. To address the matter, this article develops a personalised data publishing method for multiple SAs. According to the requirements of individuals, the new method partitions SAs values into two categories: private values and public values, and breaks the association between them for privacy guarantees. For the private values, this paper takes the process of anonymisation, while the public values are released without this process. An algorithm is designed to achieve the privacy mode, where the selectivity is determined by the sensitive value frequency and undesirable objects. The experimental results show that the proposed method can provide more information utility when compared with previous methods. The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary. The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

Present privacy preserving data publishing model only consider the tables with one sensitive attribute, so it isn’t match tables with multi-sensitive attributes. The approach based on multi-sensitive bucketization can protect the privacy of multi-sensitive attributes data publishing effectively, unfortunately, it is at a cost of hundreds of records being suppressed. This paper proposes an l-coverage cluster grouping model based on the same set of sensitive attributes to address the problem. On the premise of none suppressed record, a cluster algorithm is designed to implement the model. Extensive experiments illustrate that the new model can protect privacy of data with multi-sensitive attributes effectively and enforce the usability of data publishing.

ABSTRACTPrivacy preservation methods for anonymizing multiple sensitive attributes (MSA) data in the field of privacy-preserving data publishing (PPDP) mostly seek enforcement of the -diversity privacy model on MSA coupled with quasi-identifier (QID) generalization and tuple suppression, resulting in high data degradation of the published releases. Most existing work produces static releases that are not dynamic and web-based. In this article, we propose KC-Slice, which is amodified LKC-privacy model and slicing technique, for anonymizing MSA data dynamically, to produce releases that preserve the dataset content from most attack models and reduce data degradation, through cell suppression and QID random permutation. Experimental results and evaluation using data metrics and information entropy show remarkable reduction in data degradation and suppression ratio.

The recent evolution of the Internet of Things in the healthcare sector has attained substantial recognition from the government and industry. Healthcare data accumulated from diverse sources are stored by health service providers which is useful for patient diagnosis as well as for research for pivotal analysis. However, healthcare data contains sensitive information of an individual that needs to be protected. The sensitive health information of an individual contains multiple attributes and the correlation of this information may lead to a privacy breach. In more complex scenarios, such type of data is expected to be released periodically and dynamically, the privacy breach of individuals becomes imminent due to the presence of personal data. In this article, a fuzzy logic-based intelligent privacy-aware algorithm is proposed to protect the individual’s privacy with multiple sensitive attributes in a dynamic data release scenario. Our formal modeling and analysis show that the proposed approach offers a robust privacy guarantee while releasing health-related data. Furthermore, the empirical evaluation exhibits that the proposed model outperforms the state-of-the-art approaches in terms of information loss and query accuracy.