• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.5
• /
• pp.987-999
• /
• 2021

Recently, Federated learning has become an issue due to privacy invasion caused by data. Federated learning is safe from privacy violations because it does not need to be collected into a server and does not require learning data. As a result, studies on application methods for utilizing distributed devices and data are underway. However, Federated learning is no longer safe as research on the reconstruction attack to restore learning data from gradients transmitted in the Federated learning process progresses. This paper is to verify numerically and visually how well data reconstruction attacks work in various data situations. Considering that the attacker does not know how the data is constructed, divide the data with the class from when only one data exists to when multiple data are distributed within the class, and use MNIST data as an evaluation index that is MSE, LOSS, PSNR, and SSIM. The fact is that the more classes and data, the higher MSE, LOSS, and PSNR and SSIM are, the lower the reconstruction performance, but sufficient privacy invasion is possible with several reconstructed images.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.2
• /
• pp.227-239
• /
• 2023

Online marketing is a rapidly growing industry that heavily depends on digital technologies and data analysis to effectively reach and engage consumers. For that, artificial intelligence (AI) has emerged as a crucial tool for online marketers, enabling marketers to analyze extensive consumer data and automate decision-making processes. The purpose of this study was to investigate the ethical implications of using AI in online marketing, focusing on its impact on consumer privacy and decision-making. AI has created new possibilities for personalized marketing but raises concerns about the collection and use of consumer data, transparency and accountability of decision-making, and the impact on consumer autonomy and privacy. In this study, we reviewed the relevant literature and case studies to assess the potential risks and make recommendations for improving consumer protection. The findings provide insights into ethical considerations and offer a roadmap for balancing the advantages of AI in online marketing with the protection of consumer rights. Companies should consider these ethical issues when implementing AI in their marketing strategies. In this study, we explored the concerns and provided insights into the challenges posed by AI in online marketing, such as the collection and use of consumer data, transparency, and accountability of decision-making, and the impact on consumer autonomy and privacy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.7
• /
• pp.3319-3338
• /
• 2016

Cloud storage is becoming more and more popular because of its elasticity and pay-as-you-go storage service manner. In some cloud storage scenarios, the data that are stored in the cloud may be shared by a group of users. To verify the integrity of cloud data in this kind of applications, many auditing schemes for shared cloud data have been proposed. However, all of these schemes do not consider the access authorization problem for users, which makes the revoked users still able to access the shared cloud data belonging to the group. In order to deal with this problem, we propose a novel public auditing scheme for shared cloud data in this paper. Different from previous work, in our scheme, the user in a group cannot any longer access the shared cloud data belonging to this group once this user is revoked. In addition, we propose a new random masking technique to make our scheme preserve both data privacy and identity privacy. Furthermore, our scheme supports to enroll a new user in a group and revoke an old user from a group. We analyze the security of the proposed scheme and justify its performance by concrete implementations.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.23 no.4
• /
• pp.767-779
• /
• 2013

A field of privacy protection lacks statistical information about the current status, compared to other fields. On top of that, since it has not been classified as a concrete separate field, the related survey is only conducted as a part of such concrete areas. Furthermore, this trend of being regarded as a part of fields such as informatization, information protection and law will continue in the near future. In this paper, a novel and practical way for collecting and storing a big amout of data from 110,000 privacy policies by data controller is proposed and the real analysis results is also shown. The proposed method can save time and cost compared with the traditional survey-based method while maintaining or even advancing the accuracy of results and speediness of process. The collected big personal data can be used to set up various kinds of statistical models and they will play an important role as a breakthrough of observing the present status of privacy information protection policy. The big data concept is incorporated into the privacy protection and we can observe the method and some results throughout the paper.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.175-184
• /
• 2014

The purpose of this study is to examine the factors influencing purchase intention of Location-Based Services (LBS) using privacy-trust-behavioral intention model. This model tests various theoretical research hypotheses relating to LBS, privacy-trust-behavioral intention model, and Concern for Information Privacy(CFIP). The target population of this study was LBS users. Data for this study were collected from January 21 to March 20, 2014. The data were gathered from 231 questionnaire respondents with experience using LBS. Among these reponses, 21 were excluded because of missing or inappropriate data. After removing the unsuitable questionnaires, a total of 210 surveys were considered for analysis. The results of hypothesis testing are as follows. First, location awareness positively influence privacy concerns. Second, privacy concerns negatively influence trust. Finally, trust positively influence purchase intention. The results of this study will provide various implication to improve purchase intention of LBS.

• International Journal of Contents
• /
• v.13 no.1
• /
• pp.22-30
• /
• 2017

The diffusion of advanced mobile technology has introduced new types of personal information or 'location data'. These new data mean new opportunities for businesses, such as location-based services (LBS), but have resulted in new consumer anxieties regarding disclosure of personal information. This study examines the effects of the consumers' perceived control over "time-andplace" information in location-aware services on their perceived privacy risk. A total of 270 respondents participated in this study. Conditions of perceived privacy control were operationalized over time-and-place information, in a $2{\times}2$ factorial design. Results indicate that the perceived control over time-and-place personal information is a significant predictor of perceived risk, and control assurances over time-and-place information enhances the perception of control, thus alleviating the perceived risk. In addition, the effect is much more significant when time and place were combined.

• Journal of the Korean Home Economics Association
• /
• v.27 no.1
• /
• pp.59-69
• /
• 1989

The sutdy is to investigate the variables that influenced on territoriality$.$privacy and to find the relation between territoriality$.$privacy and self-identity. Data were collected from 342 homemakers in Pusan. Data were analysed by SPSS programs. To test hypotheses frequency, correlation and Multiple Regression (Path Analysis) were applied. The results were as follows: 1. The significant variales that influenced on territoriality were space occupancy level, SES and neighborhood relationship. 2. The significant variables that influenced on privacy were space occupancy level, SES and neighborhood relationship. 3. The significant variables that influenced on self-identity were territoriality, privacy, neighborhood relationship, housing ownership, space occupancy level and SES.

• Annual Conference of KIPS
• /
• 2016.04a
• /
• pp.461-463
• /
• 2016

Information security and preservation of customer's data privacy are key factors for further wide spread adoption of microgrid (MG) technology. However, strong coupling between the operation cost of multi-microgrid (MMG) system and privacy of customer data makes it more challenging. A nested energy management system (EMS) has been proposed in this paper. The surplus/shortage information from the inner level MGs is included in processing the optimal operation of outer level MGs. This type of optimization ensures a layered privacy-preservation to customer at each MG level. The proposed EMS architecture is a better trade-off architecture between the operation cost of the MMG system and customer privacy-preservation at each level of MG.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.2
• /
• pp.450-470
• /
• 2023

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure. Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services. Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content. Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment. Experimental results show the algorithm has high security, retrieval accuracy and efficiency.

• International Journal of Computer Science & Network Security
• /
• v.23 no.7
• /
• pp.49-60
• /
• 2023

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution.