• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1428-1434
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• 2006

In this paper, we propose the scheme solving privacy infringement in RFID systems with improving the scalability of back-end server. With RFID/USN becoming important subject, many approaches have been proposed and applied. However, limits of RFID, low computation power and storage, make the protection of privacy difficult. The Hash Chain scheme has been known as one guaranteeing forward security, confidentiality and indistinguishability. In spite of that, it is a problem that requires much of computation to identify tags in Back-End server. In this paper, we introduce an efficient key search method, the Hellman Method, to reduce computing complexity in Back-End server. Hellman Method algorism progresses pre-computation and (re)search. In this paper, after applying Hellman Method to Hash chain theory, We compared Preservation and key reference to analyze and apply to parallel With guaranteeing requistes of security for existing privacy protecting Comparing key reference reduced computation time of server to reduce computation complex from O(m) to $O(\frac{m{^2/3}}{w})$ than the existing form.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.183-196
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• 2011

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.209-219
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• 2010

Online social network services that are rapidly growing recently store tremendous data and analyze them for many research areas. To enhance the effectiveness of information, companies or public institutions publish their data and utilize the published data for many purposes. However, a social network containing information of individuals may cause a privacy disclosure problem. Eliminating identifiers such as names is not effective for the privacy protection, since private information can be inferred through the structural information of a social network. In this paper, we consider a new complex attack type that uses both the content and structure information, and propose a model, $\ell$-degree diversity, for the privacy preserving publication of the social network data against such attacks. $\ell$-degree diversity is the first model for applying $\ell$-diversity to social network data publication and through the experiments it shows high data preservation rate.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• 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 Communications and Networks
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• v.13 no.2
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• pp.102-112
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• 2011

In this paper, we propose a privacy-preserving emergency call scheme, called PEC, enabling patients in life-threatening emergencies to fast and accurately transmit emergency data to the nearby helpers via mobile healthcare social networks (MHSNs). Once an emergency happens, the personal digital assistant (PDA) of the patient runs the PEC to collect the emergency data including emergency location, patient health record, as well as patient physiological condition. The PEC then generates an emergency call with the emergency data inside and epidemically disseminates it to every user in the patient's neighborhood. If a physician happens to be nearby, the PEC ensures the time used to notify the physician of the emergency is the shortest. We show via theoretical analysis that the PEC is able to provide fine-grained access control on the emergency data, where the access policy is set by patients themselves. Moreover, the PEC can withstandmultiple types of attacks, such as identity theft attack, forgery attack, and collusion attack. We also devise an effective revocation mechanism to make the revocable PEC (rPEC) resistant to inside attacks. In addition, we demonstrate via simulation that the PEC can significantly reduce the response time of emergency care in MHSNs.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.115-122
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• 2021

In the recent past enormous enterprise applications have migrated into the cloud computing (CC). The researchers have contributed to this ever growing technology and as a result several innovations strengthened to offer the quality of service (QoS) as per the demand of the customer. It was treated that management of resources as the major challenge to offer the QoS while focusing on the trade-offs among the performance, availability, reliability and the cost. Apart from these regular key focuses to meet the QoS other key issues in CC are data integrity, privacy, transparency, security and legal aspects (DIPTSL). This paper aims to carry out the literature survey by reflecting on the prior art of the work with regard to QoS in CC and possible implementation of block chain to implement decentralised CC solutions governing DIPTSL as an integral part of QoS.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.607-614
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• 2009

Recently, Lu et al. proposed an efficient conditional privacy preservation protocol, named ECPP, based on group signature scheme for generating anonymous certificates from roadside units (RSUs). However, ECPP does not provide unlinkability and traceability when multiple RSUs are compromised. In this paper, we make up for the limitations and propose a robust and efficient anonymous authentication protocol without loss of efficiency as compared with ECPP. Furthermore, in the proposed protocol, RSUs can issue multiple anonymous certificates to an OBU to alleviate system overheads for mutual authentication between OBUs and RSUs. In order to achieve these goals, we consider a universal re-encryption scheme and identity-based key establishment scheme as our building blocks. Several simulations are conducted to verify the efficiency and effectiveness of the proposed protocol by comparing with those of the existing ECPP.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.209-212
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• 2012

최근 소셜 네트워크 서비스의 인기가 높아짐과 더불어 유저의 프라이버시에 대한 관심도 증가하고 있다. 기존의 소셜 네트워크 서비스는 중앙 집중형 구조를 가지고 있으므로 모든 유저의 프라이버시 정보와 행동들은 서비스 제공자에게 수집되어 진다. 본 논문에서는 중앙 집중식 구조의 무분별한 정보 수집을 제거하고자 오픈 소스를 이용한 P2P 기반 분산형 소셜 네트워크 서비스를 제안한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.526-533
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• 2016

The researches protecting user's location in location-based services(LBS) have received much attention. Especially k-anonymity is the most popular privacy preservation method. k-anonymization means that it selects k-1 other dummies or clients to make the cloaking region. This reduced the probability of the query issuer's location being exposed to untrusted parties to 1/k. But query's location may expose to adversary when k-1 dummies are concentrated in query's location or there is dummy in where query can not exist. Therefore, we proposed the dummy system model and algorithm taking the real environment into account to protect user's location privacy. And we proved the efficiency of our method in terms of experiment result.