HaRim Jung

Jun Pyo Park

Hyunsik Choi

Jihoon Son

MinKyoung Sung

Sungmin Yi

SooHyung Kim

Byungnam Lim

Changbeom Shim

Ilhyun Suh

Hyukki Lee

Wooil Kim

Jongseon Kim

Dalsu Choi

Minsoo Kim

Kijung Jung

Hyunsik Yoon

YongHyun Cho

Byung Ku Cho

Young Mo Kwon

Ji Min Kim

Yongsung Kim

Haemi Yang

Eunok Shin

Hyoseok Ryu

Yeeun Shim

Wan Heo

Jonghun Kim

Kunwoo Lee

Deokyeon Jang

고려대학교 DX 연구실에서 함께 연구하실 분을 모집합니다
모집 대상:
대학원생, 학부연구생
지원 자격:
COSE 371, COSE 372 과목 A0 이상 학생

Privacy-preserving ECG data collection for arrhythmia classification

Sleepwatcher: Detecting Sleep Apnea/Hypopnea Syndrome from Wearable Devices Using Deep Learning

Empirical Evaluation of Acquisition Functions for Bayesian Optimization-based Configuration Tuning of Apache Spark Applications

Differentially Private and Explainable Boosting Machine with Enhanced Utility

LFT: A Machine Unlearning Method with Layer-wise Fine Tuning

Evaluating and Improving Join Strategy Selection in Spark SQL

A Strategy for Balancing Update Latency and Read Latency in Data Lakehouses using Vertical Table Partitioning

QHB + : Accelerated Configuration Optimization for Automated Performance Tuning of Spark SQL Applications

A Differential-Privacy Technique for Publishing Density-based Clustering Results

LAFD: Local-Differentially Private and Asynchronous Federated Learning with Direct Feedback Alignment

Demonstrating Waffle: A Self-driving Grid Index

Group Tracking for Video Monitoring Systems: A Spatio-temporal Query Processing Approach

CellTrackVis: interactive browser-based visualization for analyzing cell trajectories and lineages

A Sleep Apnea Detection Method using Heart Rate Variability Data

CPS+: Configuration Auto-Tuning for Interactive Data Exploration Applications on Spark SQL

An automatic metadata configuration system for electronic documents in Republic of Korea Army Onnara system

A Perturbation-based Anonymization Method for Asynchronous GPS Trajectory Data

An Adapter Fusion Based Machine Unlearning Methodology for Decoder Models

Multi-dimensional Data Compression and Query Processing in Array Databases

Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System

ReSKY: Efficient Subarray Skyline Computation in Array Databases

Subarray Skyline Query Processing in Array Databases

IDRA: An In-storage Data Reorganization Accelerator for Multidimensional Databases

Successive Point-of-Interest Recommendation with Local Differential Privacy

SkyFlow: A visual analysis of high-dimensional skylines in time-series

Differentially Private Neural Networks with Bounded Activation Function

Biological Cell Tracking and Lineage Inference via Random Finite Sets

A Survey of Differential Privacy-based Techniques and their Applicability to Location-Based Services

Cohesive Ridesharing Group Queries in Geo-Social Networks

Automatic monitoring of service reliability for web applications: a simulation‐based approach

MARS: A multi-level array representation for simulation data

Differentially private release of medical microdata: an efficient and practical approach for preserving informative attribute values

DimensionSlice: A main-memory data layout for fast scans of multidimensional data

Safe contact tracing for COVID-19: A method without privacy breach using functional encryption techniques based-on spatio-temporal trajectory data

차분 프라이버시 – 프라이버시 보호 빅데이터 처리 기준

Finding COVID-19 Possible Cases Based on Card Payment Data: A Method Without Privacy Breaches using Functional Encryption Techniques

Knowledge Graph based Point-of-interest Recommendation Using Attention Mechanism

Progressive Top-k Subarray Query Processing in Array Databases

Moving view field nearest neighbor queries

Nearest Close Friend Search in Geo-social Networks

An anonymization protocol for continuous and dynamic privacy-preserving data collection

Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing

View-field & Category Constrained Nearest Neighbor Search in Criminal Surveillance Systems

A Visual Analysis System for Sensor Data

A Time-series Visualization of Skylines on Multi-dimensional Data

A Visual Explorer for Analyzing Trajectory Patterns

Reverse View Field Nearest Neighbor queries

Utility-preserving anonymization for health data publishing

CATS: cache-aware task scheduling for Hadoop-based systems

ℓ-Close Range Friends Query on Social Grid Index

Privacy-preserving data cube for electronic medical records: An experimental evaluation

프라이버시 보호 데이터 배포를 위한 모델 조사

2PTS: A Two-Phase Task Scheduling Algorithm for MapReduce

A Workload Assignment Strategy for Efficient ROLAP Data Cube Computation in Distributed Systems

SSFile: A novel column-store for efficient data analysis in Hadoop-based distributed systems

QR-tree: An efficient and scalable method for evaluation of continuous range queries

View ﬁeld nearest neighbor: A novel type of spatial queries

A framework to preserve the privacy of electronic health data streams

A Parallel Maximal Matching Algorithm for Large Graphs Using Pregel

Load Balancing for Real-Time, Location-Based Event Processing on Cloud Systems

Tajo: A Distributed Data Warehouse System on Large Clusters (demo)

Lineage Encoding: An Efficient Wireless XML Streaming Supporting Twig Pattern Queries

SPQI: An Efficient Index for Continuous Range Queries in Mobile Environments

Skyline queries on keyword-matched data

Processing generalized k-nearest neighbor queries on a wireless broadcast stream

Skew-Tolerant Key Distribution for Load Balancing in MapReduce

A privacy Protection Method for Social Network Data against Content/Degree Attacks

On Processing View Field Nearest Neighbor Queries on the R-tree

On Processing Scored k-Dominant Skyline Queries

Monitoring continuous k-nearest neighbor queries in the hybrid wireless network

Parallel Data Processing with MapReduce: A Survey

Hierarchically Organized Skew-Tolerent Histograms for Geographic Data Objects

On Processing Location Based Top-kQueries in the Wireless Broadcasting System

An Efficient Method for Maintaining Data Cubes Incrementally

Energy and Latency Efficient Processing of Full-text Searches on a Wireless Broadcast Stream

Energy and Latency Efficient Access of Wireless XML Stream

SPIDER : A System for Scalable, Parallel / Distributed Evaluation of large-scale RDF Data

Generalized Spatial Queries in the Wireless Data Broadcasting System

Design and Implementation of MLC NAND Flash-based DBMS for Mobile Devices

An Effective Self-Adaptive Admission Control Algorithm for Large Web Caches

POT:An Efficient Top-K Monitoring Method for Spatially Correlated Sensor Readings

Direction-based Clustering for Efficient Multicasting in Wireless Ad-Hoc Networks

Temporal Aggregation using Corner Transformations

A Cache Invalidation Scheme for Continuous Partial Match Queries in Mobile Computing Environments

A request distribution method for clustered VOD servers considering buffer sharing effects

Energy-Efficient Processing of Complex Queries on a Wireless Broadcast Stream

Secure Similarity Search

A Lightweight, Uni-directional Routing Protocol for Event-driven Sensor Networks

XIR: Cache Invalidation Strategy for XML Data in Mobile Environments

In-network Processing for Skyline Queries in Sensor Networks

An Indexing Method for Wireless Broadcast XML Data

An Efficient, Robust Method for Processing of Partial Top-k/Bottom-k Queries using the RD-Tree in OLAP

An Indexing Scheme for Energy-efficient Processing of Content-based Retrieval Queries on a Wireless Data Stream

A Wireless Broadcast Generation Scheme considering Data Access Frequencies

Data Storage in Sensor Networks for Multi-dimensional Range Queries

Efficient Stream Organization for Wireless Broadcasting of XML Data

Multi-attribute Hashing of Wireless Data Stream for Content Retrieval Queries

Rotational Lease: Providing High Availability in a Shared Storage File System

Fractionalized View Materialization in Data Cubes

Predicate-based Caching in Mobile Clients for Continuous Partial Match Queries

Parallel data processing with MapReduce: a survey

Effective data placement for wireless broadcast

A survey of differential privacy-based techniques and their applicability to location-based services

QEM: A scheduling method for wireless broadcast data

SPIDER: a system for scalable, parallel/distributed evaluation of large-scale RDF data

An index replication scheme for wireless data broadcasting

Energy-and latency-efficient processing of full-text searches on a wireless broadcast stream

Tajo: A distributed data warehouse system on large clusters

Secure similarity search

In-network processing for skyline queries in sensor networks

Efficient stream organization for wireless broadcasting of XML data

An indexing method for wireless broadcast XML data

Method for recognizing motion pattern and the apparatus for the same

Successive point-of-interest recommendation with local differential privacy

Lineage encoding: An efficient wireless xml streaming supporting twig pattern queries

POT: an efficient top- k monitoring method for spatially correlated sensor readings

Yon Dohn Chung

QEM: A Scheduling Method for Wireless Broadcast Data

A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency per node, and simple abstraction. This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then introduce its optimization strategies reported in the recent literature. We also discuss the open issues and challenges raised on parallel data analysis with MapReduce.

Background Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k -anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. Methods We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2 …

This paper investigates how to place data objects on air for wireless broadcast such that mobile clients can access the data in short latency. We first define and analyze the problem of wireless data placement, and also propose a measure, named Query Distance ( QD ), which represents the coherence degree of data set accessed by a query. We show that the problem is NP -complete, and then propose an effective data placement method that constructs the broadcast schedule by appending each query's data set in greedy way. We show through performance experiments that the proposed method reduces the access time of mobile query.

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has led users to constantly generate various location data during their daily activities. Consequently, a growing interest has been seen in location-based services (LBSs), which aim to provide services adjusted to the current locations of users. However, location information may contain sensitive data; therefore, most users are reluctant to provide their exact location data to service providers. This has been identified as the most significant challenge in LBSs. Recently, differential privacy (DP) has emerged as a de facto standard for privacy-preserving data processing. With its strong privacy guarantees, DP has been used in diverse areas such as the collection, analysis, and release of sensitive private data, and several variants of DP have been proposed in the literature. The main objective of this paper is to investigate the …

The anonymization of health data streams is important to protect these data against potential privacy breaches. A large number of research studies aiming at offering privacy in the context of data streams has been recently conducted. However, the techniques that have been proposed in these studies generate a significant delay during the anonymization process, since they concentrate on applying existing privacy models (e.g., k -anonymity and l -diversity) to batches of data extracted from data streams in a period of time. In this paper, we present delay-free anonymization , a framework for preserving the privacy of electronic health data streams. Unlike existing works, our method does not generate an accumulation delay, since input streams are anonymized immediately with counterfeit values. We further devise late validation for increasing the data utility of the anonymization results and managing the counterfeit …

In mobile distributed systems the data on air can be accessed by a large number of clients. This paper describes the way clients access the wireless broadcast data with short latency. We define and analyze the problem of wireless data scheduling and we propose a measure, named QueryDistance (QD), which represents the degree of coherence for the data set accessed by a query. We give a practically usable method named QEM which constructs the broadcast schedule by expanding each query's data set in greedy way. We also evaluate the performance of our method by experiments.

RDF is a data model for representing labeled directed graphs, and it is used as an important building block of semantic web. Due to its flexibility and applicability, RDF has been used in applications, such as semantic web, bioinformatics, and social networks. In these applications, large-scale graph datasets are very common. However, existing techniques are not effectively managing them. In this paper, we present a scalable, efficient query processing system for RDF data, named SPIDER, based on the well-known parallel/distributed computing framework, Hadoop. SPIDER consists of two major modules (1) the graph data loader, (2) the graph query processor. The loader analyzes and dissects the RDF data and places parts of data over multiple servers. The query processor parses the user query and distributes sub queries to cluster nodes. Also, the results of sub queries from multiple servers are gathered (and …

In mobile distributed environment, data broadcasting has many applications because it has two desirable characteristics: energy efficiency and bandwidth efficiency. There have been some researches on the indexing mechanisms for wireless data broadcasting in the past. We first describe the problematic issue in the conventional index replication scheme, and then propose various index replication methods based on three criteria: accessibility, energy efficiency and adjacency. We evaluate the proposed scheme in analytic and experimental ways. We show that the proposed index replication scheme reduces the data access cost of mobile clients in an energy efficient manner. We also discuss the relaxation of the given criteria.

In wireless mobile computing environments, broadcasting is an effective and scalable technique to disseminate information to a massive number of clients, wherein the energy usage and latency are considered major concerns. This paper presents an indexing scheme for the energy- and latency-efficient processing of full-text searches over the wireless broadcast data stream. Although a lot of access methods and index structures have been proposed in the past for full-text searches, all of them are targeted for data in disk storage, not wireless broadcast channels. For full-text searches on a wireless broadcast stream, we firstly introduce a naive, inverted list-style indexing method, where inverted lists are placed in front of the data on the wireless channel. In order to reduce the latency overhead, we propose a two-level indexing method which adds another level of index structure to the basic inverted list-style index. In …

The increasing volumes of relational data let us find an alternative to cope with them. Recently, several hybrid approaches (e.g., HadoopDB and Hive) between parallel databases and Hadoop have been introduced to the database community. Although these hybrid approaches have gained wide popularity, they cannot avoid the choice of suboptimal execution strategies. We believe that this problem is caused by the inherent limits of their architectures. In this demo, we present Tajo, a relational, distributed data warehouse system on shared-nothing clusters. It uses Hadoop Distributed File System (HDFS) as the storage layer and has its own query execution engine that we have developed instead of the MapReduce framework. A Tajo cluster consists of one master node and a number of workers across cluster nodes. The master is mainly responsible for query planning and the coordinator for workers. The master …

One of the most substantial ways to protect users' sensitive information is encryption. This paper is about the keyword index search system on encrypted documents. It has been thought that the search with errors over encrypted data is impossible because 1 bit difference over plaintexts may reduce to enormous bits difference over cyphertexts. We propose a novel idea to deal with the search with errors over encrypted data. We develop two similarity search schemes, implement the prototypes and provide substantial analysis. We define security requirements for the similarity search over encrypted data. The first scheme can achieve perfect privacy in similarity search but the second scheme is more efficient.

Wireless sensor networks can be used in various fields, e.g., military and civil applications. The technique of saving energy to prolong the life of sensor nodes is one of main challenges to resource-constrained sensor networks. Therefore, in-network aggregation of data has been proposed in resource-constrained environments for energy efficiency. Most previous works on in-network aggregation only support a one-dimensional data (e.g., MIN and MAX). To support a multi-dimensional data, the skyline query is used. The skyline query returns a set of points that are not dominated by any other point on all dimensions. The majority of previous skyline query processing methods (e.g., BNL and BBS) work on centralized storage. Centralized query processing methods do not have merits in terms of energy efficiency in high event rate environments. In this paper, we propose new algorithm of in-network processing for the …

Introduction : The aim of this study is to evaluate the effectiveness and efficiency of privacy-preserving data cubes of electronic medical records (EMRs). An EMR data cube is a complex of EMR statistics that are summarized or aggregated by all possible combinations of attributes. Data cubes are widely utilized for efficient big data analysis and also have great potential for EMR analysis. For safe data analysis without privacy breaches, we must consider the privacy preservation characteristics of the EMR data cube. In this paper, we introduce a design for a privacy-preserving EMR data cube and the anonymization methods needed to achieve data privacy. We further focus on changes in efficiency and effectiveness that are caused by the anonymization process for privacy preservation. Thus, we experimentally evaluate various types of privacy-preserving EMR data cubes using several practical metrics and discuss the …

This paper presents a wireless streaming method for XML data which supports energy-efficient processing of queries over the stream in mobile clients. We propose new stream organizations for XML data which have different kinds of addresses to related data in the stream. We describe event-driven stream generation algorithms for the proposed stream structures and provide search algorithms for simple XML path queries which leverage the access mechanisms incorporated in the stream. Experimental results show that our approach can effectively improve the tuning time performance of user queries in a wireless broadcasting environment.

Collecting personal data without privacy breaches is important to utilize distributed microdata. Privacy-preserving data collection is anonymizing personal data within the data transmission from data holders to a data collector without privacy breaches. A number of research studies aiming at facilitating the privacy-preserving data collection have been recently conducted. However, the existing studies only allow very particular methods to anonymize data and require too strict assumptions for the private channels between the data holders and the data collector. Thus, these studies suffer from limited data utility and cannot be applied in many environments that does not support the particular requirements. In this paper, we present a novel protocol for the privacy preserving data collection. Unlike existing works, our protocol does not restrict the type of anonymization method and does not require the private channel. Our …

The paper considers a wireless information system, wherein various pieces of information represented in XML are broadcast via wireless channels, and mobile clients access the broadcast stream using energy-restricted portable devices. In this paper, we propose a wireless XML streaming method designed to provide energy-efficient access to a wireless stream. We construct two hierarchical structures to represent the XML data and their index information, called the XML data tree and XML index tree, respectively. The wireless XML stream is generated by traversing these two structures with some replications. We design three data/index replication strategies (PP, TT, and TP) in the streaming method. We compare the proposed streaming method with a naı¨ve method called the (1,X) method both analytically and experimentally. Also, based on our analysis results, we determine the optimal method of replication.

The present invention relates to a method and apparatus for recognizing a motion pattern formed by a continued contact Surface. A method for recognizing a motion patter according to an embodiment of the invention may comprise receiving a motion pattern as input from a user, comparing pattern infor mation of the motion pattern with pattern information of a preset release pattern, and determining a mismatch level of the motion pattern according to the comparison result. According to an embodiment of the invention, if an inputted motion pattern does not match the preset release pattern, the degree of mismatch is determined with different levels, to respond in various ways other than simply maintaining the locked State.

A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The existing POI recommendation systems require raw check-in history of users, which might cause location privacy violations. Although there have been several matrix factorization (MF) based privacy-preserving recommendation systems, they can only focus on user-POI relationships without considering the human movements in check-in history. To tackle this problem, we design a successive POI recommendation framework with local differential privacy, named SPIREL. SPIREL uses two types of information derived from the check-in history as input for the factorization: a transition pattern between two POIs and the visit counts of POIs. We propose a novel objective function for learning …

In this paper, we propose an energy and latency efficient XML dissemination scheme for the mobile computing. We define a novel unit structure called G-node for streaming XML data in the wireless environment. It exploits the benefits of the structure indexing and attribute summarization that can integrate relevant XML elements into a group. It provides a way for selective access of their attribute values and text content. We also propose a lightweight and effective encoding scheme, called Lineage Encoding, to support evaluation of predicates and twig pattern queries over the stream. The Lineage Encoding scheme represents the parent-child relationships among XML elements as a sequence of bit-strings, called Lineage Code(V, H), and provides basic operators and functions for effective twig pattern query processing at mobile clients. Extensive experiments using real and synthetic data sets demonstrate our …

In this paper, we discuss the top- k monitoring over sensor networks. Since sensor readings are usually correlated with location, top- k nodes are clustered at some areas. Motivated by such a characteristic, we propose a novel tree structure named partial ordered tree (POT) to efficiently maintain clusters of the highest readings. By using POTs, only candidate nodes which might be included in top- k result are evaluated for query processing. Through simulation experiments, we evaluate the performance of the POT method in comparison with conventional methods.

DataX Laboratory

교수님 소개

정연돈

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Privacy-preserving ECG data collection for arrhythmia classification

Sleepwatcher: Detecting Sleep Apnea/Hypopnea Syndrome from Wearable Devices Using Deep Learning

Empirical Evaluation of Acquisition Functions for Bayesian Optimization-based Configuration Tuning of Apache Spark Applications

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

정연돈

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Privacy-preserving ECG data collection for arrhythmia classification

Sleepwatcher: Detecting Sleep Apnea/Hypopnea Syndrome from Wearable Devices Using Deep Learning

Empirical Evaluation of Acquisition Functions for Bayesian Optimization-based Configuration Tuning of Apache Spark Applications

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집