Myung Ho Kim - Academia.edu (original) (raw)

Papers by Myung Ho Kim

The Internet of Things (IoT) is now an emerging global Internet-based information architecture us... more The Internet of Things (IoT) is now an emerging global Internet-based information architecture used to facilitate the exchange of goods and services. IoT-related applications are aiming to bring technology to people anytime and anywhere, with any device. However, the use of IoT raises a privacy concern because data will be collected automatically from the network devices and objects which are embedded with IoT technologies. In the current applications, data collector is a dominant player who enforces the secure protocol that cannot be verified by the data owners. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness data collection protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. The communication (respondents and data collector) and collaboration (among respondents) in our solution will be performed automatically.

Over the past several years, many companies have gained benefits from the implementation of cloud... more Over the past several years, many companies have gained benefits from the implementation of cloud solutions within the organization. Due to the advantages such as flexibility, mobility, and costs saving, the number of cloud users is expected to grow rapidly. Consequently, organizations need a secure way to authenticate its users in order to ensure the functionality of their services and data stored in the cloud storages are managed in a private environment. In the current approaches, the user authentication in cloud computing is based on the credentials submitted by the user such as password, token and digital certificate. Unfortunately, these credentials can often be stolen, accidentally revealed or hard to remember. In view of this, we propose a biometric-based authentication protocol to support the user authentication for the cloud environment. Our solution can be used as the second factor for the cloud users to send their authentication requests. In our design, we incorporate several players (client, service agent and service provider) to collaborate together to perform the matching operation between the query feature vector and the biometric template of the user. In particular, we consider a distributed scenario where the biometric templates are stored in the cloud storage while the user authentication is performed without the leakage of any sensitive information.

Similarity coefficients (also known as coefficients of association) are important measurement tec... more Similarity coefficients (also known as coefficients of association) are important measurement techniques used to quantify the extent to which objects resemble one another. Due to privacy concerns, the data owner might not want to participate in any similarity measurement if the original dataset will be revealed or could be derived from the final output. There are many different measurements used for numerical, structural and binary data. In this paper, we particularly consider the computation of similarity coefficients for binary data. A large number of studies related to similarity coefficients have been performed. Our objective in this paper is not to design a specific similarity coefficient. Rather, we are demonstrating how to compute similarity coefficients in a secure and privacy preserved environment. In our protocol, a client and a server jointly participate in the computation. At the end of the protocol, the client will obtain all summation variables needed for the computation while the server learns nothing. We incorporate cryptographic methods in our protocol to protect the original dataset and all other intermediate results. Note that our protocol also supports dissimilarity coefficients.

Cloud computing is an emerging technology that allows different service providers to offer servic... more Cloud computing is an emerging technology that allows different service providers to offer services in an on-demand environment. Due to the advantages such as flexibility, mobility, and costs saving, the number of cloud user has increased tremendously. Consequently, a more secure and privacy preserving authentication system is becoming important to ensure that only the data owner or the authorized user can gain access and manipulate data stored in the cloud. In the current approach, the service provider authenticates its users based on the credential submitted such as password, token and digital certificate. Unfortunately, these credentials can often be stolen, accidentally revealed or hard to remember. In view of this, we propose a biometric-based authentication protocol, which can be used as the second factor for the cloud users to send their authentication requests. In our solution, the credential submitted by the users consists of the biometric feature vector and the verification code. For the user to successful authenticate, both the biometric feature vector and the verification code must be combined, transformed, and shuffled correctly. Our proposed solution not only provides the security mechanism for the authentication process, but also supports the privacy protection for all sensitive information of the user.

Biometric-based authentication systems have been widely used in many applications that require hi... more Biometric-based authentication systems have been widely used in many applications that require high reliable scheme. However, the rapid deployment of biometric systems raises great attentions about the privacy concern. The primary concern in any biometric-based system is the leakage of user’s biometric templates stored in the server. This is of particular important because biometric characteristics for humans are limited and they cannot be reissued or changed. Once the original template has been revealed, the user’s privacy will be compromised. A malicious party might use the compromised template to gain unauthorized access to the system or for cross-matching purposes. One of the commonly used solutions for template protection is the encryption of the templates. Since the same biometric trait will not produce two identical feature vectors, the encryption of two slightly difference feature vectors will produce two distinct ciphertexts. Hence, encrypted templates must be decrypted before they can be used for comparison. Unfortunately, the decryption of encrypted templates is viewed as insecure because it is too risky to expose the original biometric template during the authentication process. We propose a privacy-preserving biometric authentication system which securely authenticates users and also protects their biometric features (both the query feature vector and template). We incorporate homomorphic encryption scheme which made the comparison possible in the encrypted domain. In our protocol, the similarity score (based on squared Euclidean distance) between the query feature vector and the biometric template is computed without the decryption of the original template. Our protocol fulfills the requirements of template protection and extra attention is paid to the advantages of using a homomorphic encryption scheme over biometric-based authentication systems. Finally, we show the correctness, security and privacy analysis of our protocol in this paper.

Two-party equality test is a scheme used to compare two or more private inputs. Under security an... more Two-party equality test is a scheme used to compare two or more private inputs. Under security and privacy concerns, the equality test needs to ensure that only the test result is revealed without leaking any extra information to any party. In this paper, we study the design of an efficient and secure protocol to facilitate the equality test in two-party setting. We further discuss the correctness and security analysis for our protocol in this paper. Our protocol requires one round interaction between players without the involvement of any trusted third party.

Knowledge‐discovering or pattern‐discovering process, such as data mining, is an important techni... more Knowledge‐discovering or pattern‐discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi‐party computation (SMC) approach has been widely used to solve privacy‐preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy‐preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions.
Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.

Data sharing is an essential process for collaborative works particularly in banking, finance and... more Data sharing is an essential process for collaborative works particularly in banking, finance and healthcare industry. These industries require many collaborative works with their internal and external parties such as branches, clients, and service providers. When data are shared among collaborators, security and privacy concerns becoming crucial issues and cannot be avoided. Privacy is an important issue that is frequently discussed during the development of collaborative systems. It is closely related with the security issues because each of them can become a treat for another. The tradeoff between privacy and security is an interesting topic that we are going to address in this paper. In view of the practical problems in the existing approaches, we propose a collaborative framework which can be used to facilitate concurrent operations, single point failure problem, and overcome constraints for two-party computation. Two secure computation protocols will be discussed to demonstrate our collaborative framework.

Advances in both sensor technologies and network infrastructures have encouraged the development ... more Advances in both sensor technologies and network infrastructures have encouraged the development of smart environments to enhance people's life and living styles. However, collecting and storing user's data in the smart environments pose severe privacy concerns because these data may contain sensitive information about the subject. Hence, privacy protection is now an emerging issue that we need to consider especially when data sharing is essential for analysis purpose. In this paper, we consider the case where two agents in the smart environment want to measure the similarity of their collected or stored data. We use similarity coefficient function (F SC ) as the measurement metric for the comparison with differential privacy model. Unlike the existing solutions, our protocol can facilitate more than one request to compute F SC without modifying the protocol. Our solution ensures privacy protection for both the inputs and the computed F SC results.

Frontiers of Information Technology & Electronic Engineering, Sep 6, 2015

Recently, privacy concerns about data collection have received an increasing amount of attention... more Recently, privacy concerns about data collection have received an increasing amount of attention. In a recent publication, a data collector (an agency) assumed that all respondents would be comfortable with submitting their data if the published data were anonymous. We believe that this assumption is not realistic because the increase in privacy concerns causes some respondents to refuse participation or to submit inaccurate data to such agencies. If respondents submit inaccurate data, then the usefulness of the results from analysis of the collected data cannot be guaranteed. Furthermore, we note that the level of anonymity (i.e., k-Anonymity) guaranteed by an agency cannot be verified by respondents since they generally do not have access to all of the data that are released. Therefore, we introduce the notion of k_i-anonymity where k_i is the level of anonymity preferred by each respondent i. Instead of placing full trust in an agency, our solution increases respondent confidence by allowing each to decide the preferred level of protection. As such, our protocol ensures that respondents achieve their preferred k_i-anonymity during data collection and guarantees that the collected records are genuine and useful for data analysis.

The Internet of Things (IoT) is now an emerging global Internet-based information architecture us... more The Internet of Things (IoT) is now an emerging global Internet-based information architecture used to facilitate the exchange of goods and services. IoT-related applications are aiming to bring technology to people anytime and anywhere, with any device. However, the use of IoT raises a privacy concern because data will be collected automatically from the network devices and objects which are embedded with IoT technologies. In the current applications, data collector is a dominant player who enforces the secure protocol that cannot be verified by the data owners. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness data collection protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. The communication (respondents and data collector) and collaboration (among respondents) in our solution will be performed automatically.

Over the past several years, many companies have gained benefits from the implementation of cloud... more Over the past several years, many companies have gained benefits from the implementation of cloud solutions within the organization. Due to the advantages such as flexibility, mobility, and costs saving, the number of cloud users is expected to grow rapidly. Consequently, organizations need a secure way to authenticate its users in order to ensure the functionality of their services and data stored in the cloud storages are managed in a private environment. In the current approaches, the user authentication in cloud computing is based on the credentials submitted by the user such as password, token and digital certificate. Unfortunately, these credentials can often be stolen, accidentally revealed or hard to remember. In view of this, we propose a biometric-based authentication protocol to support the user authentication for the cloud environment. Our solution can be used as the second factor for the cloud users to send their authentication requests. In our design, we incorporate several players (client, service agent and service provider) to collaborate together to perform the matching operation between the query feature vector and the biometric template of the user. In particular, we consider a distributed scenario where the biometric templates are stored in the cloud storage while the user authentication is performed without the leakage of any sensitive information.

Similarity coefficients (also known as coefficients of association) are important measurement tec... more Similarity coefficients (also known as coefficients of association) are important measurement techniques used to quantify the extent to which objects resemble one another. Due to privacy concerns, the data owner might not want to participate in any similarity measurement if the original dataset will be revealed or could be derived from the final output. There are many different measurements used for numerical, structural and binary data. In this paper, we particularly consider the computation of similarity coefficients for binary data. A large number of studies related to similarity coefficients have been performed. Our objective in this paper is not to design a specific similarity coefficient. Rather, we are demonstrating how to compute similarity coefficients in a secure and privacy preserved environment. In our protocol, a client and a server jointly participate in the computation. At the end of the protocol, the client will obtain all summation variables needed for the computation while the server learns nothing. We incorporate cryptographic methods in our protocol to protect the original dataset and all other intermediate results. Note that our protocol also supports dissimilarity coefficients.

Cloud computing is an emerging technology that allows different service providers to offer servic... more Cloud computing is an emerging technology that allows different service providers to offer services in an on-demand environment. Due to the advantages such as flexibility, mobility, and costs saving, the number of cloud user has increased tremendously. Consequently, a more secure and privacy preserving authentication system is becoming important to ensure that only the data owner or the authorized user can gain access and manipulate data stored in the cloud. In the current approach, the service provider authenticates its users based on the credential submitted such as password, token and digital certificate. Unfortunately, these credentials can often be stolen, accidentally revealed or hard to remember. In view of this, we propose a biometric-based authentication protocol, which can be used as the second factor for the cloud users to send their authentication requests. In our solution, the credential submitted by the users consists of the biometric feature vector and the verification code. For the user to successful authenticate, both the biometric feature vector and the verification code must be combined, transformed, and shuffled correctly. Our proposed solution not only provides the security mechanism for the authentication process, but also supports the privacy protection for all sensitive information of the user.

Biometric-based authentication systems have been widely used in many applications that require hi... more Biometric-based authentication systems have been widely used in many applications that require high reliable scheme. However, the rapid deployment of biometric systems raises great attentions about the privacy concern. The primary concern in any biometric-based system is the leakage of user’s biometric templates stored in the server. This is of particular important because biometric characteristics for humans are limited and they cannot be reissued or changed. Once the original template has been revealed, the user’s privacy will be compromised. A malicious party might use the compromised template to gain unauthorized access to the system or for cross-matching purposes. One of the commonly used solutions for template protection is the encryption of the templates. Since the same biometric trait will not produce two identical feature vectors, the encryption of two slightly difference feature vectors will produce two distinct ciphertexts. Hence, encrypted templates must be decrypted before they can be used for comparison. Unfortunately, the decryption of encrypted templates is viewed as insecure because it is too risky to expose the original biometric template during the authentication process. We propose a privacy-preserving biometric authentication system which securely authenticates users and also protects their biometric features (both the query feature vector and template). We incorporate homomorphic encryption scheme which made the comparison possible in the encrypted domain. In our protocol, the similarity score (based on squared Euclidean distance) between the query feature vector and the biometric template is computed without the decryption of the original template. Our protocol fulfills the requirements of template protection and extra attention is paid to the advantages of using a homomorphic encryption scheme over biometric-based authentication systems. Finally, we show the correctness, security and privacy analysis of our protocol in this paper.

Two-party equality test is a scheme used to compare two or more private inputs. Under security an... more Two-party equality test is a scheme used to compare two or more private inputs. Under security and privacy concerns, the equality test needs to ensure that only the test result is revealed without leaking any extra information to any party. In this paper, we study the design of an efficient and secure protocol to facilitate the equality test in two-party setting. We further discuss the correctness and security analysis for our protocol in this paper. Our protocol requires one round interaction between players without the involvement of any trusted third party.

Knowledge‐discovering or pattern‐discovering process, such as data mining, is an important techni... more Knowledge‐discovering or pattern‐discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi‐party computation (SMC) approach has been widely used to solve privacy‐preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy‐preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions.
Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.

Data sharing is an essential process for collaborative works particularly in banking, finance and... more Data sharing is an essential process for collaborative works particularly in banking, finance and healthcare industry. These industries require many collaborative works with their internal and external parties such as branches, clients, and service providers. When data are shared among collaborators, security and privacy concerns becoming crucial issues and cannot be avoided. Privacy is an important issue that is frequently discussed during the development of collaborative systems. It is closely related with the security issues because each of them can become a treat for another. The tradeoff between privacy and security is an interesting topic that we are going to address in this paper. In view of the practical problems in the existing approaches, we propose a collaborative framework which can be used to facilitate concurrent operations, single point failure problem, and overcome constraints for two-party computation. Two secure computation protocols will be discussed to demonstrate our collaborative framework.

Advances in both sensor technologies and network infrastructures have encouraged the development ... more Advances in both sensor technologies and network infrastructures have encouraged the development of smart environments to enhance people's life and living styles. However, collecting and storing user's data in the smart environments pose severe privacy concerns because these data may contain sensitive information about the subject. Hence, privacy protection is now an emerging issue that we need to consider especially when data sharing is essential for analysis purpose. In this paper, we consider the case where two agents in the smart environment want to measure the similarity of their collected or stored data. We use similarity coefficient function (F SC ) as the measurement metric for the comparison with differential privacy model. Unlike the existing solutions, our protocol can facilitate more than one request to compute F SC without modifying the protocol. Our solution ensures privacy protection for both the inputs and the computed F SC results.

Frontiers of Information Technology & Electronic Engineering, Sep 6, 2015

Recently, privacy concerns about data collection have received an increasing amount of attention... more Recently, privacy concerns about data collection have received an increasing amount of attention. In a recent publication, a data collector (an agency) assumed that all respondents would be comfortable with submitting their data if the published data were anonymous. We believe that this assumption is not realistic because the increase in privacy concerns causes some respondents to refuse participation or to submit inaccurate data to such agencies. If respondents submit inaccurate data, then the usefulness of the results from analysis of the collected data cannot be guaranteed. Furthermore, we note that the level of anonymity (i.e., k-Anonymity) guaranteed by an agency cannot be verified by respondents since they generally do not have access to all of the data that are released. Therefore, we introduce the notion of k_i-anonymity where k_i is the level of anonymity preferred by each respondent i. Instead of placing full trust in an agency, our solution increases respondent confidence by allowing each to decide the preferred level of protection. As such, our protocol ensures that respondents achieve their preferred k_i-anonymity during data collection and guarantees that the collected records are genuine and useful for data analysis.