• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1377-1391
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• 2023

When sensitive topics such as gambling habits, drug addiction, alcoholism, tax evasion tendencies, induced abortions, drunk driving, past criminal involvement, and homosexuality are the focus of open or direct surveys, it becomes challenging to obtain accurate information due to nonresponse bias and response bias. People often hesitate to provide truthful answers. Warner introduced an ingenious method to address this issue. In this study, a new and unrelated randomized response model is proposed to eliminate misleading responses and nonresponses caused by the stigma associated with the attribute being investigated. The proposed randomized response model allows for the estimation of the population percentage with the sensitive characteristic in an unbiased manner. The characteristics and recommendations of the proposed randomized response model are examined, and numerical examples are provided to support the findings of this study.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.1
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• pp.1-12
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• 2014

An access control system is needed to ensure only authorized users can access a sensitive resource. We propose a secure access control based on a fully secure and fine grained ciphertext-policy attribute-based encryption scheme. The access control for a sensitive resource is ensured by encrypting it with encryption algorithm from the CP-ABE scheme parameterized by an access control policy. Furthermore, the proposed access control supports non-monotone type access control policy. The ciphertext only can be recovered by users whose attributes satisfy the access control policy. We also implement and measure the performance of our proposed access control. The results of experiments show that our proposed secure access control is feasible.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.27-38
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• 1995

In the surveys of sensitive issues of the population that is composed of several unknown-size stratum, we propose the unrelated question model with quantitative attribute by using stratified double sampling. And, we consider two types of sample allocations under the fixed cost, which are the proportional allocation, the optimum allocation. In efficiency, the proosed model is inferior to the unrelated question model with quantitative attribute by stratified sampling in case of the size of each stratum is known. But we find that efficiency of the proposed model is increased, when the selecting probability of sensitive question p is small and first stage sample size n' is large.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1067-1082
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• 2003

In this paper, we propose a randomized response model with discrete quantitative attribute by three-stage cluster sampling for obtaining discrete quantitative data by using the Liu & Chow model(1976), when the population was made up of sensitive discrete quantitative clusters. We obtain the minimum variance by calculating the optimum number of fsu, ssu, tsu under the some given constant cost. And we obtain the minimum cost under the some given accuracy.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.753-765
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• 2001

We suggest a quantitative conditional unrelated question model that can be used in obtaining more sensitive information. For whom say "yes" about the less 7han sensitive question .B we ask only about the more sensitive variable X. We extend our model to two sample case when there is no information about the true mean of the unrelated variable Y. Finally we compare the efficiency of our model with that of Greenberg et al.′s.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2718-2731
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• 2019

Conjunctive keyword search encryption is an important technique for protecting sensitive data that is outsourced to cloud servers. However, the process of searching outsourced data may facilitate the leakage of sensitive data. Thus, an efficient data search approach with high security is critical. To solve this problem, an efficient conjunctive keyword search scheme based on ciphertext-policy attribute-based encryption is proposed for cloud storage environment. This paper proposes an efficient mechanism for removing the secure channel and resisting off-line keyword-guessing attacks. The storage overhead and the computational complexity are regardless of the number of keywords. This scheme is proved adaptively secure based on the decisional bilinear Diffie-Hellman assumption in the standard model. Finally, the results of theoretical analysis and experimental simulation show that the proposed scheme has advantages in security, storage overhead and efficiency, and it is more suitable for practical applications.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.75-82
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• 2015

We discuss confidence intervals for sensitive binomial attributes obtained by a direct question method and indirect question method. The Randomized Response Technique(RRT) by Warner (1965) is an indirect question method that uses a randomization device to reduce the response burden of respondents. We used the mean coverage probability (MCP), root mean squared error (RMSE), and mean expected width (MEW) to compare the confidence intervals by the two methods. The numerical comparisons indicated found that the MEW of RRT is too large and the RRT is so conservative that the MCP exceeds a nominal level(${\alpha}$); therefore, it is necessary to complement these problem in order to increase the utility of the indirect question method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1404-1423
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• 2015

In smart grid, electricity consumption data may be handed over to a third party for various purposes. While government regulations and industry compliance prevent utility companies from improper or illegal sharing of their customers' electricity consumption data, there are some scenarios where it can be very useful. For example, it allows the consumers' data to be shared among various energy resources so the energy resources are able to analyze the data and adjust their operation to the actual power demand. However, it is crucial to protect sensitive electricity consumption data during the sharing process. In this paper, we propose a fine-grained access control scheme (FAC) with efficient attribute revocation and policy updating in smart grid. Specifically, by introducing the concept of Third-party Auditor (TPA), the proposed FAC achieves efficient attribute revocation. Also, we design an efficient policy updating algorithm by outsourcing the computational task to a cloud server. Moreover, we give security analysis and conduct experiments to demonstrate that the FAC is both secure and efficient compared with existing ABE-based approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5100-5119
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• 2018

Most of existing privacy-preserving multi-authorities attribute-based encryption schemes (PP-MA-ABE) only considers the privacy of the user identity (ID). However, in many occasions information leakage is caused by the disclosing of his/her some sensitive attributes. In this paper, we propose a collusion-resisting ciphertext-policy PP-MA-ABE (CRPP-MACP-ABE) scheme with hiding both user's ID and attributes in the cloud storage system. We present a method to depict anonymous users and introduce a managerial role denoted by IDM for the management of user's anonymous identity certificate ($AID_{Cred}$). The scheme uses $AID_{Cred}$ to realize privacy-preserving of the user, namely, by verifying which attribute authorities (AAs) obtain the blinded public attribute keys, pseudonyms involved in the $AID_{Cred}$ and then distributes corresponding private keys for the user. We use different pseudonyms of the user to resist the collusion attack launched by viciousAAs. In addition, we utilize IDM to cooperate with multiple authorities in producing consistent private key for the user to avoid the collusion attack launched by vicious users. The proposed CRPP-MACP-ABE scheme is proved secure. Some computation and communication costs in our scheme are finished in preparation phase (i.e. user registration). Compared with the existing schemes, our scheme is more efficient.