• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3419-3437
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• 2022

Anonymization technology is an important technology for privacy protection in the process of data release. Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data. However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem. In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool. On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed. The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds. The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5731-5754
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• 2019

In this paper, we redesign, implement, and evaluate ShareSafe (Based on SecGraph), an open-source secure graph data sharing/publishing platform. Within ShareSafe, we propose De-anonymization Quantification Module and Recommendation Module. Besides, we model the attackers' background knowledge and evaluate the relation between graph data privacy and the structure of the graph. To the best of our knowledge, ShareSafe is the first platform that enables users to perform data perturbation, utility evaluation, De-A evaluation, and Privacy Quantification. Leveraging ShareSafe, we conduct a more comprehensive and advanced utility and privacy evaluation. The results demonstrate that (1) The risk of privacy leakage of anonymized graph increases with the attackers' background knowledge. (2) For a successful de-anonymization attack, the seed mapping, even relatively small, plays a much more important role than the auxiliary graph. (3) The structure of graph has a fundamental and significant effect on the utility and privacy of the graph. (4) There is no optimal anonymization/de-anonymization algorithm. For different environment, the performance of each algorithm varies from each other.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.67-70
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• 2005

현대사회에서는 자신도 알지못하는 많은 정보들이 유포된다. 이때 정보들은 개인의 익명성을 보장하기위해 성명, 성별, 주민등록번호와 같은 개인식별 애트리뷰트를 생략한채로 유포된다. 그러나 널리퍼져있는 이러한 정보들은 다른 외부 정보와 조인되므로써 유일하게 개인을 식별하게끔 하는 조인공격을 받을 수 있다. 하지만 이러한 조인공격시 여러데이터가나오게하므로써 개인식별을 어렵게 또는 불가능하게하는 방법을 k-anonymization이라고하고 이러한 k-anonymization을 지원하는 방법으로 이전부터 여러가지가 있다. 이전의 방법들로는 각 subset마다 k-anonymization을 검사해야했으나 Lefevre와 DeWitt가 제안한 Incognito 방법을 사용하면 한번의 검사로 모든k-anonymization을 보장할 수 있다. 이 논문에서는 이러한 Incognito를 bitmap index를 사용하므로써 성능을 개선시키는 기법을 제시한다.

• Journal of KIISE
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• v.44 no.1
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• pp.21-26
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• 2017

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

• Journal of KIISE
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• v.42 no.5
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• pp.681-689
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• 2015

Personal information includes information about a living human individual. It is the information identifiable through name, resident registration number, and image, etc. Personal information which is collected by institutions can be wrongfully used, because it contains confidential information of an information object. In order to prevent this, a method is used to remove personal identification elements before distributing and sharing the data. However, even when the identifier such as the name and the resident registration number is removed or changed, personal information can be exposed in the case of a linking attack. This paper proposes a new anonymization technique to enhance data utility. To achieve this, attributes that are utilized in service tend to anonymize at a low level. In addition, the anonymization technique of the proposal can provide two or more anonymized data tables from one original data table without concern about a linking attack. We also verify our proposal by using the cooperative game theory.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.197-209
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• 2011

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.9-14
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• 2014

In this paper, we propose AMV scheme which supports k-anonymization by using vectors for mobile clients. AMV can produces the minimal cloaking area using motion vector information of users (clients). The main reason for minimizing cloaking area is a server has to send the object information to all users who request the spatial queries. The experimental results show that the proposed AMV has superior performance over existing methods.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1785-1792
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• 2017

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.99-106
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• 2023

Now a days, large volumes of data is accumulating in every field due to increase in capacity of storage devices. These large volumes of data can be applied with data mining for finding useful patterns which can be used for business growth, improving services, improving health conditions etc. Data from different sources can be combined before applying data mining. The data thus gathered can be misused for identity theft, fake credit/debit card transactions, etc. To overcome this, data mining techniques which provide privacy are required. There are several privacy preserving data mining techniques available in literature like randomization, perturbation, anonymization etc. This paper proposes an Enhanced Hybrid Privacy Preserving Data Mining(EHPPDM) technique. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy. The experimental results show that classification accuracies have increased using EHPPDM technique.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.19-20
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• 2010