• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.210-222
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• 2011

In this work, we study the problem of privacy-preservation data publishing in moving objects databases. In particular, the trajectory of a mobile user in a plane is no longer a polyline in a two-dimensional space, instead it is a two-dimensional surface of fixed width $2A_{min}$, where $A_{min}$ defines the semi-diameter of the minimum spatial circular extent that must replace the real location of the mobile user on the XY-plane, in the anonymized (kNN) request. The desired anonymity is not achieved and the entire system becomes vulnerable to attackers, since a malicious attacker can observe that during the time, many of the neighbors' ids change, except for a small number of users. Thus, we reinforce the privacy model by clustering the mobile users according to their motion patterns in (u, ${\theta}$) plane, where u and ${\theta}$ define the velocity measure and the motion direction (angle) respectively. In this case, the anonymized (kNN) request looks up neighbors, who belong to the same cluster with the mobile requester in (u, ${\theta}$) space: Thus, we know that the trajectory of the k-anonymous mobile user is within this surface, but we do not know exactly where. We transform the surface's boundary poly-lines to dual points and we focus on the information distortion introduced by this space translation. We develop a set of efficient spatiotemporal access methods and we experimentally measure the impact of information distortion by comparing the performance results of the same spatiotemporal range queries executed on the original database and on the anonymized one.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.7-17
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• 2019

Machine learning is one of the most accurate techniques to predict and analyze various phenomena. K-means clustering is a kind of machine learning technique that classifies given data into clusters of similar data. Because it is desirable to perform an analysis based on a lot of data for better performance, K-means clustering can be performed in a model with a server that calculates the centroids of the clusters, and a number of clients that provide data to server. However, this model has the problem that if the clients' data are associated with private information, the server can infringe clients' privacy. In this paper, to solve this problem in a model with a number of clients, we propose a privacy-preserving K-means clustering method that can perform machine learning, concealing private information using homomorphic encryption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.11-26
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• 2011

The existing anonymous authentication schemes based on group signatures for protecting privacy do not provide anonymous authorization which is required in the practical environments. In this paper, we propose an anonymous authentication and authorization scheme that enables a service provider both to authenticate anonymously its users and to provide different service according to their authorization. In the proposed scheme, a user's real identity, anonymity and authorization are managed distinctly through the separation of group manager's capabilities and an authorization authority. It is also possible for the proposed scheme to apply various access control models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.251-260
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• 2010

Through the convergence of medical services and the IT technique, the patient's personal health information computerization has been rapidly spread with propagation of electronic medical record(EHR). In addition, by entering u-health, the demand of the secondary use for public health, medical research, and medical service using electronic patient health care records are increasing. The personal health information secondary uses for the development of academic medical area and service, are very good thing. But, carelessly to use personal health information, the patient privacy would be damaged. However, there are not yet systematic studies about secondary use of personal health information. Therefore, in this paper, we analyze the difference of the internal and external bill for personal medical data secondary use and propose the direction of the medical service development and preservation of the individual's privacy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.139-149
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• 2008

The amount of personal information collected by organizations and government agencies is continuously increasing. When a data collector publishes personal information for research and other purposes, individuals' sensitive information should not be revealed. On the other hand, published data is also required to provide accurate statistical information for analysis. k-Anonymity and ${\iota}$-diversity models are popular approaches for privacy preserving data publication. However, they are limited to static data release. After a dataset is updated with insertions and deletions, a data collector cannot safely release up-to-date information. Recently, the m-invariance model has been proposed to support re-publication of dynamic datasets. However, the m-invariant generalization can cause high information loss. In addition, if the adversary already obtained sensitive values of some individuals before accessing released information, the m-invariance leads to severe privacy disclosure. In this paper, we propose a novel technique for safely releasing dynamic datasets. The proposed technique offers a simple and effective method for handling inserted and deleted records without generalization. It also gives equivalent degree of privacy preservation to the m-invariance model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5135-5149
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• 2015

Communications among multi-party must be fast, cost effective and secure. Today's computing environments such as internet conference, multi-user games and many more applications involve multi-party. All participants together establish a common session key to enable multi-party and secure exchange of messages. Multi-party password-based authenticated key exchange scheme allows users to communicate securely over an insecure network by using easy-to-remember password. Kwon et al. proposed a practical three-party password-based authenticated key exchange (3-PAKE) scheme to allow two users to establish a session key through a server without pre-sharing a password between users. However, Kwon et al.'s scheme cannot meet the security requirements of key authentication, key confirmation and anonymity. In this paper, we present a novel, simple and efficient multi-party password-based authenticated key exchange (M-PAKE) scheme based on the elliptic curve cryptography for mobile environment. Our proposed scheme only requires two round-messages. Furthermore, the proposed scheme not only satisfies security requirements for PAKE scheme but also achieves efficient computation and communication.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.562-566
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• 2016

클라우드 컴퓨팅의 발전과 더불어 데이터베이스 아웃소싱에 대한 연구가 활발히 진행되고 있다. 그러나 기존 정보보호를 지원하는 Topk 질의처리 연구는 다양한 형태로 정보가 노출되는 문제점을 보인다. 따라서 본 논문에서는 데이터 보호, 사용자 질의 보호, 데이터 접근 패턴 은닉을 모두 지원하는 아웃소싱 데이터베이스 상에서의 안전한 Topk 질의처리 알고리즘을 제안한다. 성능평가를 통해, 제안하는 기법이 정보보호를 지원하는 동시에 효율적인 성능을 제공함을 보인다.

• Journal of Families and Better Life
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• v.22 no.5
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• pp.101-111
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• 2004

This study was conducted to examine how the types of housing values influence the preference for computerized housing (so called 'Intelligent Housing'). A questionnaire survey was conducted on 347 residents in Seoul metropolitan area. The following findings were obtained: 1) The housing values were classified into 6 types: ostentation, information & technology, tradition preservation, personality, practicality, and privacy pursuit. 2) There were significant differences in housing values by the generation and the level of familiarity with information technology. 3) In general, computerized housing was highly preferred, and the housing value types that affected the preference were 'ostentation' and 'information & technology.' 4) There is a need to develop diverse models of computerized housing systems to respond to consumer demands.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.285-291
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• 2022

With the growing exchange of data between individuals and institutions through various electronic communication, valuable data protection is in high demand to ensure that it is not hacked and that privacy is protected. Many security techniques, such as encryption and steganography, have emerged to prevent security breaches. The purpose of this research is to integrate cryptographic and steganography techniques to secure text message sending. The Rijndael algorithm was used to encrypt the text message, and the Least Significant Bit algorithm was also used to hide the encrypted message in a color image. Experiments on the suggested method have proven that it can improve the security of sent messages due to the human eye's inability to identify the original image from the image after it has been covered, as well as the encryption of the message using a password.