Search : [ author: 김종선 ] (4)

A Privacy-preserving Histogram Construction Method Guaranteeing the Differential Privacy

In Cheol Baek, Jongseon Kim, Yon Dohn Chung

http://doi.org/10.5626/JOK.2022.49.6.488

With the widespread use of data collection and analysis, the need for preserving the privacy of individuals is emerging. Various privacy models have been proposed to guarantee privacy while collecting and analyzing data in a privacy-preserving manner. Among various privacy models, the differential privacy stands as the de facto standard. In this paper, we propose a privacy-preserving histogram construction method guaranteeing differential privacy. The proposed method consists of histogram bin setting and frequency calculation stages. In the first stage, we use the Laplace mechanism to heuristic bin setting algorithms to select a differentially private number of bins. In the second stage, we use the Laplace mechanism to each frequency falling into the bins to output differentially private frequencies. We prove the proposed method guarantees differential privacy and compare the accuracy according to privacy budget values and distribution rates through experiments.

Privacy-preserving Pre-computation of Join Selectivity using Differential Privacy for the Proliferation of Pseudonymized Data Combination

Hyubjin Lee, Jong Seon Kim, Yon Dohn Chung

http://doi.org/10.5626/JOK.2022.49.3.250

With the enforcement of 3 data acts, pseudonymized information from various domains can be joined through certified expert agencies. Before joining all pseudonymized information, the expert agency provides a service that can compute the join selectivity in advance. However, the existing join selectivity pre-computation methods have vulnerabilities that can lead to privacy breaches. In this paper, we propose a privacy-preserving join selectivity pre-computation method that uses randomly generated one-time key values provided by the expert agency for anonymizing data through a one-way hash technique, and ensures differential privacy when pre-computing join selectivity. The proposed method ensures the anonymity of the data sent by the join requesting institutions to the expert agency and prevents privacy breaches that may occur in the previous join selectivity pre-computation methods. The experimental results showed that the proposed method provided effective join selectivity while satisfying differential privacy.

Graph Embedding-Based Point-Of-Interest Recommendation Considering Weather Features

Kun Woo Lee, Jongseon Kim, Yon Dohn Chung

http://doi.org/10.5626/JOK.2022.49.3.221

As the Location-Based Services (LBS) grow rapidly, the Point-Of-Interest (POI) recommendation becomes an active research area to provide users appropriate information relevant to their locations. Recently, translation-based recommendation systems using graph embedding, such as TransRec, are attracting great attention. In this paper, we discovered some drawbacks of TransRec; it is limited in expressing the complex relationship between users and POIs, and the relation embedding is fixed without considering weather features. We propose WAPTRec, a graph embedding-based POI recommendation method considering the weather, that overcomes the drawback of TransRec. WAPTRec can rep resent the same POI embedding in different ways according to users by using a category projection matrix and attention mechanism. In addition, it provides better recommendation accuracy by utilizing the users’ movement history, category of POIs and weather features. Experiments using public datasets illustrated that WAPTRec outperformed the conventional translation-based recommendation methods.

Models for Privacy-preserving Data Publishing : A Survey

Jongseon Kim, Kijung Jung, Hyukki Lee, Soohyung Kim, Jong Wook Kim, Yon Dohn Chung

http://doi.org/

In recent years, data are actively exploited in various fields. Hence, there is a strong demand for sharing and publishing data. However, sensitive information regarding people can breach the privacy of an individual. To publish data while protecting an individual’s privacy with minimal information distortion, the privacy- preserving data publishing(PPDP) has been explored. PPDP assumes various attacker models and has been developed according to privacy models which are principles to protect against privacy breaching attacks. In this paper, we first present the concept of privacy breaching attacks. Subsequently, we classify the privacy models according to the privacy breaching attacks. We further clarify the differences and requirements of each privacy model.


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