• Title/Summary/Keyword: Preserving Information

Search Result 858, Processing Time 0.025 seconds

Edge Preserving Smoothing in Infrared Image using Relativity of Guided Filter

  • Kim, Il-Ho
    • Journal of the Korea Society of Computer and Information
    • /
    • v.23 no.12
    • /
    • pp.27-33
    • /
    • 2018
  • In this paper, we propose an efficient edge preserving smoothing filter for Infrared image that can reduce noise while preserving edge information. Infrared images suffer from low signal-to-noise ratio, low edge detail information and low contrast. So, detail enhancement and noise reduction play crucial roles in infrared image processing. We first apply a guided image filter as a local analysis. After the filtering process, we optimization globally using relativity of guided image filter. Our method outperforms the previous methods in removing the noise while preserving edge information and detail enhancement.

Direction Presentation of Design on Privacy Preserving Mechanism for Location-Sharing Based Services (위치공유기반 서비스의 프라이버시 보호 방안의 설계 방향 제시)

  • Kim, Mihui
    • The Journal of the Korea Contents Association
    • /
    • v.15 no.2
    • /
    • pp.101-108
    • /
    • 2015
  • Location-sharing based service (LSBS) refers to a service that users share their location information with other users with whom friendship. At this time, the location information is shared through service provider, and then their position information is exposed to the service provider. The exposure of this personal position information to the service provider has raised a privacy problem, and thus privacy preserving mechanisms have been proposed to protect them. In this paper, we examine the types and features of the proposed location-sharing based services so far, and survey the research trend of privacy preserving mechanisms for them. Through the analysis on existing privacy preserving mechanisms, we present design factors for a privacy preserving mechanism for the current LSBS services, and suggest future work.

Privacy-Preserving k-Bits Inner Product Protocol (프라이버시 보장 k-비트 내적연산 기법)

  • Lee, Sang Hoon;Kim, Kee Sung;Jeong, Ik Rae
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.23 no.1
    • /
    • pp.33-43
    • /
    • 2013
  • The research on data mining that can manage a large amount of information efficiently has grown with the drastic increment of information. Privacy-preserving data mining can protect the privacy of data owners. There are several privacy-preserving association rule, clustering and classification protocols. A privacy-preserving association rule protocol is used to find association rules among data, which is often used for marketing. In this paper, we propose a privacy-preserving k-bits inner product protocol based on Shamir's secret sharing.

Practical Privacy-Preserving DBSCAN Clustering Over Horizontally Partitioned Data (다자간 환경에서 프라이버시를 보호하는 효율적인 DBSCAN 군집화 기법)

  • Kim, Gi-Sung;Jeong, Ik-Rae
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.20 no.3
    • /
    • pp.105-111
    • /
    • 2010
  • We propose a practical privacy-preserving clustering protocol over horizontally partitioned data. We extend the DBSCAN clustering algorithm into a distributed protocol in which data providers mix real data with fake data to provide privacy. Our privacy-preserving clustering protocol is very efficient whereas the previous privacy-preserving protocols in the distributed environments are not practical to be used in real applications. The efficiency of our privacy-preserving clustering protocol over horizontally partitioned data is comparable with those of privacy-preserving clustering protocols in the non-distributed environments.

Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms

  • Gunes, Ihsan;Bilge, Alper;Polat, Huseyin
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.7 no.5
    • /
    • pp.1272-1290
    • /
    • 2013
  • Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.

Development of a Privacy-Preserving Big Data Publishing System in Hadoop Distributed Computing Environments (하둡 분산 환경 기반 프라이버시 보호 빅 데이터 배포 시스템 개발)

  • Kim, Dae-Ho;Kim, Jong Wook
    • Journal of Korea Multimedia Society
    • /
    • v.20 no.11
    • /
    • pp.1785-1792
    • /
    • 2017
  • Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.

An Efficient New Format-Preserving Encryption Algorithm to encrypt the Personal Information (개인정보암호화에 효율적인 새로운 형태보존암호화 알고리즘)

  • Song, Kyung-Hwan;Kang, Hyung-Chul;Sung, Jae-Chul
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.24 no.4
    • /
    • pp.753-763
    • /
    • 2014
  • Recently financial institutions and large retailers have a large amount of personal information leakage accident occurred one after another, and the damage is a trend of increasing day by day. Regulation such as enforcing the encryption of the personal identification information are strengthened. Efficient technology to encrypt personal information is Format-preserving encryption. Typical encryption expand output data length than input data length and change a format. Format Preserving Encryption is an efficient method to minimize database and application modification, because it makes preserve length and format of input data. In this paper, to encrypt personal information efficiently, we propose newly Format Preserving Encryption using Block cipher mode of operation.

Privacy-Preserving IoT Data Collection in Fog-Cloud Computing Environment

  • Lim, Jong-Hyun;Kim, Jong Wook
    • Journal of the Korea Society of Computer and Information
    • /
    • v.24 no.9
    • /
    • pp.43-49
    • /
    • 2019
  • Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

Design of EEG Signal Security Scheme based on Privacy-Preserving BCI for a Cloud Environment (클라우드 환경을 위한 Privacy-Preserving BCI 기반의 뇌파신호 보안기법 설계)

  • Cho, Kwon;Lee, Donghyeok;Park, Namje
    • Journal of KIISE
    • /
    • v.45 no.1
    • /
    • pp.45-52
    • /
    • 2018
  • With the advent of BCI technology in recent years, various BCI products have been released. BCI technology enables brain information to be transmitted directly to a computer, and it will bring a lot of convenience to life. However, there is a problem with information protection. In particular, EEG data can raise issues about personal privacy. Collecting and analyzing big data on EEG reports raises serious concerns about personal information exposure. In this paper, we propose a secure privacy-preserving BCI model in a big data environment. The proposed model could prevent personal identification and protect EEG data in the cloud environment.

A Privacy-preserving Image Retrieval Scheme in Edge Computing Environment

  • Yiran, Zhang;Huizheng, Geng;Yanyan, Xu;Li, Su;Fei, Liu
    • 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.