Prevent Identity Disclosure Research Articles

Enhancing Utility in Anonymized Data against the Adversary’s Background Knowledge

Recent studies have shown that data are some of the most valuable resources for making government policies and business decisions in different organizations. In privacy preserving, the challenging task is to keep an individual’s data protected and private, and at the same time the modified data must have sufficient accuracy for answering data mining queries. However, it is difficult to implement sufficient privacy where re-identification of a record is claimed to be impossible because the adversary has background knowledge from different sources. The k-anonymity model is prone to attribute disclosure, while the t-closeness model does not prevent identity disclosure. Moreover, both models do not consider background knowledge attacks. This paper proposes an anonymization algorithm called the utility-based hierarchical algorithm (UHRA) for producing k-anonymous t-closed data that can prevent background knowledge attacks. The proposed framework satisfies the privacy requirements using a hierarchical approach. Finally, to enhance utility of the anonymized data, records are moved between different anonymized groups, while the requirements of the privacy model are not violated. Our experiments indicate that our proposed algorithm outperforms its counterparts in terms of data utility and privacy.

An anonymization-based privacy-preserving data collection protocol for digital health data.

Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as "leader collusion", in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.

Local generalization and bucketization technique for personalized privacy preservation

Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories of attributes, namely explicit-identifier, quasi-identifier (QI), and sensitive attribute. In general, individuals may have different views on the sensitivity of different attributes. Therefore, there is another type of attribute that contains both QI values and sensitive values, termed semi-sensitive attribute. In this paper, we propose a new anonymization technique, called Local Generalization and Bucketization, to prevent identity disclosure and protect the sensitive values on each semi-sensitive attribute and sensitive attribute. The rationale is to use local generalization and local bucketization to divide the tuples into local equivalence groups and partition the sensitive values into local buckets, respectively. The protections of local generalization and local bucketization are independent, so that they can be implemented by appropriate algorithms without weakening other protection. Besides, the protection of local bucketization for each semi-sensitive attribute and sensitive attribute is also independent. Consequently, local bucketization can comply with various principles in different attributes according to the actual requirements of anonymization. We conducted extensive experiments to illustrate the effectiveness of the proposed approach.

Social Networks Privacy Preservation: A Novel Framework

The development of several popular social networks and the publication of social networks’ data have led to the risk of leakage of sensitive and confidential information of individuals. This requires the preservation of privacy before the publication of a user’s data available from his Online Social Network (OSN) presence. Numerous algorithms have been proposed in the area of preserving the privacy of social network users’ information such as K-anonymity and L-diversity. Previous work has shown good results based on the concept of adding edges and noise nodes for achieving K-anonymity and L-diversity. K-anonymization techniques are able to prevent identity disclosure of users but are not sufficient to prevent the disclosure of sensitive information of users. In this direction, a number of techniques for preserving the sensitive information of social network users have been proposed. Although these techniques have shown reasonably good results to achieve anonymity, but they also lead to a substantial change in the original structure of the OSNs. In this article, the problems of preventing sensitive attribute disclosure and reducing the noisy nodes have been addressed by perturbing the sensitive attributes. Existing research uses L-diversity for preventing sensitive attribute disclosure resulting in skewness and similarity attacks. We have addressed the skewness attacks by removing the duplicate noisy nodes from the final dataset to be published for stakeholders by the OSN service providers. All the information of duplicate nodes has been stored in a table named Reference Attribute Table (RAT). This table will be accessible only to the service providers for the purpose of de-anonymizing the data of users. The proposed technique has been extensively evaluated using five metrics viz. APL, ACSPL, RRTI, number of noisy nodes, and information loss using four real-time datasets collected for OSNs namely CORA, ARNET, DBLP, and Twitter. Results of evaluation parameters viz. APL and RRTI show that there is less change in the structure of datasets after anonymization. Results of ACSPL show that our proposed technique is able to preserve sensitive attributes in the datasets. The maximum number of noisy nodes amongst all four datasets is 5.4% and the maximum information loss is 2.2%. Evaluation results make it evident that our proposed technique ensures privacy preservation with less loss of information and thus preserving the utility of published data.

Privacy Preservation in Resource-Constrained IoT Devices Using Blockchain—A Survey

With opportunities brought by Internet of Things (IoT), it is quite a challenge to assure privacy preservation when a huge number of resource-constrained distributed devices is involved. Blockchain has become popular for its benefits, including decentralization, persistence, immutability, auditability and consensus. With the implementation of blockchain in IoT, the benefits provided by blockchain can be derived in order to make IoT more efficient and maintain trust. In this paper, we discuss some applications of IoT in different fields and privacy-related issues faced by IoT in resource-constrained devices. We discuss some applications of blockchain in vast majority of areas, and the opportunities it brings to resolve IoT privacy limitations. We, then, survey different researches based on the implementation of blockchain in IoT. The goal of this paper is to survey recent researches based on the implementation of blockchain in IoT for privacy preservation. After analyzing the recent solutions, we see that the blockchain is an optimal way for preventing identity disclosure, monitoring, and providing tracking in IoT.

Practical anonymity models on protecting private weighted graphs

Identity disclosure control (IDC) on graph data has attracted increasing interest in security and database communities. Most existing work focuses on preventing identity disclosure derivable from certain structural information in unweighted graphs. In weighted graphs, when the weight of an edge implying relevance/association between its adjacency vertices is taken into account, this problem becomes more complex due to the diversity of weight-related information which may expose to many types of background knowledge attacks and hence significantly increases the time complexity for preventing privacy breaches. This paper systematically studies IDC in weighted graphs, which has no known solution to our knowledge, by employing elementary weight invariants as background knowledge. We propose a general anonymity model against weight-related attacks, and introduce a new utility metric based on spectral graph theory. Then we distinguish two types of practical breaches, namely volume and histogram attack, which the adversary has the knowledge of the sum and the set of adjacent weights for each vertex respectively. We propose an efficient method for volume anonymization, and a heuristic scheme for histogram anonymization which we show to be NP-hard. We show how to construct the graph under these anonymized properties to protect a graph from both attacks. Our approaches are effective in terms of efficiency and data utility preservation: run in near-quadratic time on graph size, and preserve a similar utility as the original graph. The performances of the algorithms have been validated by extensive experiments on both synthetic and real-world datasets.

Preventing Identity Disclosure in Social Networks Using Intersected Node

Social networks like Facebook, Twitter, Pinterest etc. provide data of its users to the demanding organizations to better comprehend the quality of their potential clients. Publishing confidential data of social network users in its raw form raises several privacy and security concerns. Recently, some anonymization techniques have been developed to address these issues. In this paper, a technique to prevent identity disclosure through structure attacks has been proposed which not only prevents identity disclosure but also preserves utility of data published by online social networks. Algorithms have been developed by using noise nodes/edges with the consideration of introducing minimum change in the original graphical structure of social networks. The authors' work is unique in the sense that previous works are based on edge editing only but their proposed work protects against structure attacks using mutual nodes in the social network and the effectiveness of the proposed technique has been proved using APL (Average Path Length) and information loss as parameters.

De-Identification of Health Data in Big Data using a Novel Bio-Inspired Apoptosis Algorithm

In the last years, with the emergence of new technologies in the image of big data, the privacy concerns had grown widely. However, big data means the dematerialization of the data. The classical security solutions are no longer efficient in this case. Nowadays, sharing the data is much easier as well as saying hello. The amount of shared data over the web keeps growing from day to another which creates a wide gap between the purpose of sharing data and the fact that these last contain sensitive information. For that, the researches turned their attention to new issues and domains in order to minimize this gap. In other way, they intended to ensure a good utility of data by preserving its meaning while hiding sensitive information to prevent identity disclosure. Many techniques had been used for that. Some of it is mathematical and other ones using data mining algorithms. This paper deals with the problem of hiding sensitive data in shared structured medical data using a new bio-inspired algorithm from the natural phenomena of apoptosis cells in human body.

Anonymizied Approach to Preserve Privacy of Published Data Through Record Elimination

Data mining is the process of analyzing data. Data Privacy is collection of data and dissemination of data. Privacy issues arise in different area such as health care, intellectual property, biological data, financial transaction etc. It is very difficult to protect the data when there is transfer of data. Sensitive information must be protected. There are two kinds of major attacks against privacy namely record linkage and attribute linkage attacks. Research have proposed some methods namely k-anonymity, l-diversity, t-closeness for data privacy. K-anonymity method preserves the privacy against record linkage attack alone. It is unable to prevent address attribute linkage attack. l-diversity method overcomes the drawback of k-anonymity method. But it fails to prevent identity disclosure attack and attribute disclosure attack. t-closeness method preserves the privacy against attribute linkage attack but not identity disclosure attack. A proposed method used to preserve the privacy of individuals' sensitive data from record and attribute linkage attacks. In the proposed method, privacy preservation is achieved through generalization by setting range values and through record elimination. A proposed method overcomes the drawback of both record linkage attack and attribute linkage attack

Protecting Privacy Against Record Linkage Disclosure: A Bounded Swapping Approach for Numeric Data

Record linkage techniques have been widely used in areas such as antiterrorism, crime analysis, epidemiologic research, and database marketing. On the other hand, such techniques are also being increasingly used for identity matching that leads to the disclosure of private information. These techniques can be used to effectively reidentify records even in deidentified data. Consequently, the use of such techniques can lead to individual privacy being severely eroded. Our study addresses this important issue and provides a solution to resolve the conflict between privacy protection and data utility. We propose a data-masking method for protecting private information against record linkage disclosure that preserves the statistical properties of the data for legitimate analysis. Our method recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The partition is made orthogonal to the maximum variance dimension represented by the first principal component in each partitioned set. The attribute values of a record in a subset are then masked using a double-bounded swapping method. The proposed method, which we call multivariate swapping trees, is nonparametric in nature and does not require any assumptions about statistical distributions of the original data. Experiments conducted on real-world data sets demonstrate that the proposed approach significantly outperforms existing methods in terms of both preventing identity disclosure and preserving data quality.