New privacy preserving clustering methods for secure multiparty computation

Shigei

Makino

et al. 2017

AIR

Self Cite

Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

Section: Q-learning For Secure Multiparty Computationmentioning

confidence: 99%

“…Therefore, SMC for shared data has been considered. In Miyajima, et al, [17,22] learning methods for SMC of BP, fuzzy system and VQ methods have been proposed and the validity of them has been proved. On the other hand, though there are some studies on privacy preserving with RL, [18][19][20][21] they are ones of cryptogram algorithms.…”

Section: Introductionmentioning

confidence: 99%

A proposal of privacy preserving reinforcement learning for secure multiparty computation

Shigei

Makino

et al. 2017

AIR

Self Cite

Wireless Communications and Mobile Computing

“…Anonymity techniques for data publishing have been used in the relational data for a long time, and make great progress in relational database area, including k-anonymity, l-diversity, generalization, and so forth [1,2]. Can we apply the same anonymous techniques that apply to relational data to social networks?…”

Section: Introductionmentioning

confidence: 99%

A Personalized $(α, β, l, k)$ -Anonymity Model of Social Network for Protecting Privacy

Ren

Jiang

2022

By mining the data published on social network, we can discover the hidden value of information including the privacy of individuals and organizations. Protecting privacy of individuals and organizations on social network has become the focus of more and more researchers. Based on the actual privacy protection need of edge sensitive attribute and vertexes sensitive attribute, we propose a new personalized α , β , l , k -anonymity technology of privacy preserving to reduce distortion extent of the data in the privacy processing of data of social network. Experimental results of personalized α , β , l , k -anonymity algorithm show that d -neighborhood attack of graph, background knowledge attack, and homogeneity attack can be prevented effectively by using anonymous vertexes and edges, as well as the influence matrix based on background knowledge. The diversity of vertex sensitive attribute can be achieved. Personalized protecting privacy requirements can be met by using such parameter as α , β , l , k .

“…Then, how should machine learning be realized on the edge system while protecting personal information? SMC is one of the typical models to realize machine learning safety [3][4][5]. The basic idea of SMC is to implement the data processing by dividing a dataset into subsets, processing the subset on each server, and integrating the results.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, for machine learning using SDM (Steepest Descent Method), secure learning methods can be realized. Horizontally partitioned data (HPD) and VPD are known for SMC [4,5]. The former is a method consisting of dividing a dataset into subsets, and the latter is a method consisting of dividing the dataset into element-separated subsets.…”

Section: Introductionmentioning

confidence: 99%

Simplified security learning using vertically partitioned data with IoT

Shigei

et al. 2021

NOLTA

Self Cite

Edge (or fog) computing is known as a method for improving the conventional cloud system. The basic idea is to consider a system that places edges (servers) between the cloud and the terminals (things). Then, how should machine learning be realized on the edge system? Fast and secure learning methods are desired for machine learning. Secure systems using distributed processing have attracted attention. SMC (Secure Multiparty Computation) is one of the typical models to realize secure learning. Horizontally and vertically partitioned data are known for SMC. The latter is a method consisting of dividing the dataset into element-separated subsets. It is desired to develop a method for directly executing learning using element-separated subsets. Vertically partitioned data (VPD) is considered to be a data structure that realizes such learning. In the previous papers, we proposed learning methods for BP (Back Propagation) and NG (Neural Gas) using VPD. There, we did not consider about the amount of data transferred between servers. In this paper, simplified learning methods that eliminate wasteful data transfer compared to the method in the previous papers are proposed, and its effectiveness is shown. That is, the data transfer from the central server to each server was reduced to 1/L, where L is the number of training data.