• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.3
• /
• pp.558-575
• /
• 2013

The purpose of a data leakage prevention system is to protect corporate information assets. The system monitors the packet exchanges between internal systems and the Internet, filters packets according to the data security policy defined by each company, or discretionarily deletes important data included in packets in order to prevent leakage of corporate information. However, the problem arises that the system may monitor employees' personal information, thus allowing their privacy to be violated. Therefore, it is necessary to find not only a solution for detecting leakage of significant information, but also a way to minimize the leakage of internal users' personal information. In this paper, we propose two models for representing the level of personal information disclosure during data leakage detection. One model measures only the disclosure frequencies of keywords that are defined as personal data. These frequencies are used to indicate the privacy violation level. The other model represents the context of privacy violation using a private data matrix. Each row of the matrix represents the disclosure counts for personal data keywords in a given time period, and each column represents the disclosure count of a certain keyword during the entire observation interval. Using the suggested matrix model, we can represent an abstracted context of the privacy violation situation. Experiments on the privacy violation situation to demonstrate the usability of the suggested models are also presented.

• Journal of KIISE
• /
• v.41 no.9
• /
• pp.707-714
• /
• 2014

In general, the benign apps transmit privacy information to the external to provide service to users as the malicious app does. In other words, the behavior of benign apps is similar to the one of malicious apps. Thus, the benign app can be easily manipulated for malicious purposes. Therefore, the malicious apps as well as the benign apps should notify the users of the possibility of privacy information leakage before installation to prevent the potential malicious behavior. In this paper, We propose the method to detect leakage of privacy information on the android app by analyzing API inter-dependency and shortest distance. Also, we present LeakDroid which detects leakage of privacy information on Android with the above method. Unlike dynamic approaches, LeakDroid analyzes Android apps on market site. To verify the privacy information leakage detection of LeakDroid, we experimented the well-known 250 malicious apps and the 1700 benign apps collected from Android Third party market. Our evaluation result shows that LeakDroid reached detection rate of 96.4% in the malicious apps and detected 68 true privacy information leakages inside the 1700 benign apps.

• Review of Korea Contents Association
• /
• v.14 no.3
• /
• pp.25-29
• /
• 2016

• Journal of Digital Convergence
• /
• v.14 no.12
• /
• pp.1-12
• /
• 2016

Today, with the popularity of smart phones and the proliferation of SNS, anyone is exposed to the risk of personal information leakage. Unlike the prior studies of privacy, this research aims to identify the privacy factors affecting the provision intention of individual information from the SNS Users. This study also analyses how the perceived privacy risks and corporate trust affect the provision intention of individual information. The analysis results of empirical data show that despite experiencing the privacy leakage such as direct hacking and being aware of the risk, people are providing firms with personal information. The most influential variables to perceived privacy risk are information privacy policy, information privacy concern, previous privacy experience and information privacy awareness in the decreasing order of importance. Those to the corporate trust are information privacy policy, information privacy awareness, previous privacy concern and information privacy experience. Besides, the corporate trust and the perceived privacy risk also affect the provision intention of personal information. Finally, this study proposes the implications for personal information privacy.

• The Journal of Society for e-Business Studies
• /
• v.19 no.3
• /
• pp.65-89
• /
• 2014

Although many studies have focused on the influences and outcomes of personal information leakage, few studies have investigated how the personal information leakage prevention behavior differs depending on internet user. This study attempts to supplement the existing studies' limitations with the use of risk perception attitude (RPA) framework. More specifically, this study tries to show internet user can be classified into four groups based on perceived risk of personal information leakage and efficacy beliefs of personal information protection, and to identify how the groups differ in terms of motivation, information seeking, and behaviors for privacy leakage prevention. Analysis on survey data from 276 internet users reveals that the users can be classified into responsive, avoidance, proactive, indifference groups. Furthermore, there are differences between groups in terms of motivation, information seeking, and behaviors for personal information leakage prevention. This study contributes to expand existing literature by providing tailored guidelines for implementation of personal information protection strategies and policy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2778-2791
• /
• 2017

In the age of big data, distributed data providers need to ensure the privacy, while data analysts need to mine the value of data. Therefore, how to find the privacy-utility tradeoff has become a research hotspot. Besides, the adversary may have the background knowledge of the data source. Therefore, it is significant to solve the privacy-utility tradeoff problem in the distributed environment with side information. This paper proposes a distributed privacy-utility tradeoff method using distributed lossy source coding with side information, and quantitatively gives the privacy-utility tradeoff region and Rate-Distortion-Leakage region. Four results are shown in the simulation analysis. The first result is that both the source rate and the privacy leakage decrease with the increase of source distortion. The second result is that the finer relevance between the public data and private data of source, the finer perturbation of source needed to get the same privacy protection. The third result is that the greater the variance of the data source, the slighter distortion is chosen to ensure more data utility. The fourth result is that under the same privacy restriction, the slighter the variance of the side information, the less distortion of data source is chosen to ensure more data utility. Finally, the provided method is compared with current ones from five aspects to show the advantage of our method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.5
• /
• pp.945-956
• /
• 2020

The publication of data is essential in order to receive high quality services from many applications. However, if the original data is published as it is, there is a risk that sensitive information (political tendency, disease, ets.) may reveal. Therefore, many research have been proposed, not the original data but the synthetic data generating and publishing to privacy preserve. but, there is a risk of privacy leakage still even if simply generate and publish the synthetic data by various attacks (linkage attack, inference attack, etc.). In this paper, we propose a synthetic data generation algorithm in which privacy preserved by applying differential privacy the latest privacy protection technique to GAN, which is drawing attention as a synthetic data generative model in order to prevent the leakage of such sensitive information. The generative model used CGAN for efficient learning of labeled data, and applied Rényi differential privacy, which is relaxation of differential privacy, considering the utility aspects of the data. And validation of the utility of the generated data is conducted and compared through various classifiers.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.5
• /
• pp.1103-1113
• /
• 2012

DLP technology has been effectively enforcing privacy protection policy in on-line computing environment. However, with wide spread use of smart devices and promotion of smart-works, it has been pointed out that DLP technology cannot effectively prevent privacy leakage at smart devices and is comparatively weak at APT attack. In this paper, we suggests a hybrid approach, PPS, which integrates E-DRM system with DLP technology, taking advantages of both technologies. The technology basically uses encryption function and access control of E-DRM system, and thus it can effectively prevent leakage of privacy information of customers, even if the documents are in the hands of malicious third parties.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.3
• /
• pp.151-159
• /
• 2014

Recently, spread of large amount Smartphones made users to download location-based service applications, which provided by application developers. These location-based service applications are convenient tool for users. Location-based service use technology to find location of user and provide information of user's location. Leakage of information of user's location and expose of privacy life raised new controversy. In this thesis, it will analyze relations of increase of Smartphone market, usage of Location-based service and severity of personal information leakage. Also, it will analyze examples of user's case of damage which caused by leakage personal information and find solutions to reduce damage of personal information leakage. In research, it will find cases of damage that cause by Location-based service. Also it will analyze and research cases of damage and present with graph and chart. In conclusion, to reduce and prevent from damage which caused by leakage personal information, it is important that users and application developers to realize danger of private and personal information leakage. Also, user's personal information must deal with cautiously and application developers have to research and develop the application with powerful security.

• Knowledge Management Research
• /
• v.17 no.1
• /
• pp.17-47
• /
• 2016

People use social network services (SNSs) for various purposes, for example, to share information, to express themselves, and to strengthen social relationships with others. Meanwhile, problems of personal information leakage and privacy infringement become a social issue and thus information privacy concerns about inappropriate collection and use of personal information increase in SNSs (e.g., Facebook). Although people have privacy concerns in SNSs, they behave in a contradictory way, voluntarily presenting themselves and disclosing personal information. This phenomenon is called 'privacy paradox'. Using dual factor theory, this study investigates the phenomenon of privacy paradox by looking into enablers (e.g., self-presentation) and inhibitors (e.g., information privacy concern) for continuous use of Facebook that can co-exist independently of each other. It also includes antecedents of self-presentation (i.e., narcissism, need for affiliation, involvement, and self-efficacy) and those of information privacy concern (i.e., previous experience of privacy infringement, privacy awareness, and privacy control). The results show that most hypothesized relationships were supported. These findings provide strategic implications for SNS providers who want to promote continuous use of SNS. Under the current circumstances in which enhanced privacy policies are required, this study would be a useful reference for future research on information privacy issues in SNSs.