• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.1
• /
• pp.36-44
• /
• 2022

Reversible data hiding techniques have been developed to hide confidential data in the image by shifting the histogram of the image. These techniques have a weakness in which the security of hidden confidential data is weak. In this paper, to solve this drawback, we propose a technique of triple encrypting confidential data using pixel value information and hiding it in the cover image. When confidential data is triple encrypted using the proposed technique and hidden in the cover image to generate a stego-image, since encryption based on pixel information is performed three times, the security of confidential data hidden by triple encryption is greatly improved. In the experiment to measure the performance of the proposed technique, even if the triple-encrypted confidential data was extracted from the stego-image, the original confidential data could not be extracted without the encryption keys. And since the image quality of the stego-image is 48.39dB or higher, it was not possible to recognize whether confidential data was hidden in the stego-image, and more than 30,487 bits of confidential data were hidden in the stego-image. The proposed technique can extract the original confidential data from the triple-encrypted confidential data hidden in the stego-image without loss, and can restore the original cover image from the stego-image without distortion. Therefore, the proposed technique can be effectively used in applications such as military, medical, digital library, where security is important and it is necessary to completely restore the original cover image.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.204-210
• /
• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• Journal of Korea Society of Industrial Information Systems
• /
• v.11 no.1
• /
• pp.29-37
• /
• 2006

Due to the spread of mobile communication with the development of computer network, nowadays various types of multimedia data play an important role in many areas such as entertainments, culture contents, e-commerce or medical science. But for the real application of these data, the security in the course of saving or transferring them through the public network should be assured. In this sense, many encryption algorithm have been developed and utilized. Nonetheless, most of them have focused on the text data. So they may not be suitable to the multimedia application because of their large size and real time constraint. In this paper, a chaotic map has been employed to create a symmetric stream type of encryption scheme which may be applied to the digital images with a large amounts of data. Then an efficient algebraic encryption algorithm based on the elementary operations of the Boolean matrix and image data characteristics.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.6 no.6
• /
• pp.103-110
• /
• 2016

The recent frequent cases of damage due to leakage of medical data and the privacy of medical patients is increasing day by day. The government says the Privacy Rule regulations established for these victims, such as prevention. Medical data guidelines can be seen 'national medical privacy guidelines' is only released. When replacing the image data between the institutions it has been included in the image file (JPG, JPEG, TIFF) there is exchange of data in common formats such as being made when the file is leaked to an external file there is a risk that the exposure key identification information of the patient. This medial image file has no protection such as encryption, This this paper, introduces a masking technique using a mosaic technique encrypting the image file contains the application to optical character recognition techniques. We propose pseudonymization technique of personal information in the image data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.3
• /
• pp.947-971
• /
• 2022

The Electronic Medical Record (EMR) is a valuable source of medical data intelligence in e-health systems. The watermarking techniques have been used to authenticate the owner and protect the EMR from illegal copying. The existing distributive strategies, successfully operated to secure the EMR, are found to be inadequate. Blockchain technology, mainly, is employed by a sharing database that allows the digital crypto-currency. It rapidly leads to the magnified expectations acme. In this excitement, the use of consortium adopting the technology based on Blockchain, in the EMR structure, is found improving. This type of consortium adds an immutable share with a translucent record of the entire business and it is accomplished with responsibility, along with faith and transparency. The combination of watermarking and Blockchain technology provides a singular chance to promote a secured, trustworthy electronic documents administration to share with the e-records system. The authors, in this article, present their views on consortium Blockchain technology which is incorporated in the EMR system. The ledger, used for the distribution of the block structure, has team healthcare models based on dissimilar multiple image watermarking techniques.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.3
• /
• pp.179-186
• /
• 2013

PACS is a medical system for digital medical images, and PACS expand to web-based service using public network, DICOM files should be protected from the man-in-the-middle attack because they have personal medical record. To solve the problem, we designed flexible secure transmission system using IPSec and adopted to a web-based three-dimensional medical image system. And next, we performed the performance evaluation changing integrity and encryption algorithm using DICOM volume dataset. At that time, combinations of the algorithm was 'DES-MD5', 'DES-SHA1', '3DES-MD5', and '3DES-SHA1, and the experiment was performed on our test-bed. In experimental result, the overall performance was affected by encryption algorithms than integrity algorithms, DES was approximately 50% of throughput degradation and 3DES was about to 65% of throughput degradation. Also when DICOM volume dataset was transmitted using secure transmission system, the network performance degradation had shown because of increased packet overhead. As a result, server and network performance degradation occurs for secure transmission system by ensuring the secure exchange of messages. Thus, if the secure transmission system adopted to the medical images that should be protected, it could solve server performance gradation and compose secure web PACS.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2012.05a
• /
• pp.336-339
• /
• 2012

본 논문에서는 비선형 MLCA(Maximum Length Cellular Automata)와 1D CAT(One-Dimensional Cellular Automata Transform)를 이용하여 의료 영상 암호화 방법을 제안한다. 암호화 방법은 먼저, Wolfram Rule 행렬에 의해 전이행렬 T를 생성한다. 그 후, 암호화하려는 원 영상에 생성된 전이 행렬 T를 곱하여 원 영상의 픽셀 값을 변환한다. 또한 변환된 원 영상을 여원 벡터 F와 XOR 연산하여 비선형 MLCA가 적용된 영상으로 변환한다. 다음, 게이트웨이 값을 설정하여 1D CAT 기저함수를 생성한다. 그리고, 비선형 MLCA가 적용된 영상에 생성된 1D CAT 기저함수를 곱하여 암호화를 한다. 마지막으로 키 공간 분석을 통하여 제안한 방법이 높은 암호화 수준의 성질을 가졌음을 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.958-979
• /
• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.12
• /
• pp.156-163
• /
• 2012

Recently, Information Hiding Technology are becoming increasingly demanding in the field of international security, military and medical image This paper proposes data hiding technique utilizing parity checker for gray level image. many researches have been adopted LSB substitution and XOR operation in the field of steganography for the low complexity, high embedding capacity and high image quality. But, LSB substitution methods are not secure through it's naive mechanism even though it achieves high embedding capacity. Proposed method replaces LSB of each pixel with XOR(between the parity check bit of other 7 MSBs and 1 Secret bit) within one pixel. As a result, stego-image(that is, steganogram) doesn't result in high image degradation. Eavesdropper couldn't easily detect the message embedding. This approach is applying the concept of symmetric-key encryption protocol onto steganography. Furthermore, 1bit of symmetric-key is generated by the self-reference of each pixel. Proposed method provide more 25% embedding rate against existing XOR operation-based methods and show the effect of the reversal rate of LSB about 2% improvement.

• Journal of Digital Convergence
• /
• v.17 no.7
• /
• pp.285-292
• /
• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.