Secure Rating Computation on Weighted Signed Network for Supply Chain Network

• Item type: Journal(1)
• 公開日: 2024-09-15
• タイトル: Secure Rating Computation on Weighted Signed Network for Supply Chain Network
• タイトル / 言語: en
• タイトル: Secure Rating Computation on Weighted Signed Network for Supply Chain Network
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:サプライチェーンを安全にするサイバーセキュリティ技術] social network, sharing economy, secret sharing, secure computing, trust, rating, graph
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Fujitsu Limited
• 著者所属: Fujitsu Limited
• 著者所属: Fujitsu Limited
• 著者所属: Fujitsu Limited
• 著者所属(英): en
• 著者所属(英): Fujitsu Limited
• 著者所属(英): en
• 著者所属(英): Fujitsu Limited
• 著者所属(英): en
• 著者所属(英): Fujitsu Limited
• 著者所属(英): en
• 著者所属(英): Fujitsu Limited
• 著者名: Yoshiyuki, Sakamaki; Takeru, Fukuoka; Junpei, Yamaguchi; Masanobu, Morinaga
• 著者名(英): Yoshiyuki, Sakamaki; Takeru, Fukuoka; Junpei, Yamaguchi; Masanobu, Morinaga
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A social network is a social structure formed by the participants and their relationships. There are many studies and research papers regarding the various social networks. We regard a peer-to-peer supply chain network as a social network. On social networks, nodes' (participants') metrics regarding reputation and reliability are helpful information for forming and improving their relationships. Many researchers have proposed various mathematical models and node metrics on social networks. A network modeled as an edge-weighted directed graph is called a weighted signed network (WSN). We assume each node subjectively evaluates other nodes related to itself by scores and therefore refer to them as subjective scores. We obtain a weighted signed network by relating subjective scores to edge weights. There are many studies of methods to calculate the reputation and reliability metrics of nodes from the viewpoint of a whole network by using these subjective scores. However, subjective scores of each node tend to be confidential information for a person and organization. Nodes therefore wish to keep their scores confidential. This paper proposes exponentially convergent scores called 2-fairness and 2-goodness, for nodes of weighted signed networks and proposes a secure rating computation for them that keeps each node's subjective scores and related information secret.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); DOI http://dx.doi.org/10.2197/ipsjjip.32.710; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A social network is a social structure formed by the participants and their relationships. There are many studies and research papers regarding the various social networks. We regard a peer-to-peer supply chain network as a social network. On social networks, nodes' (participants') metrics regarding reputation and reliability are helpful information for forming and improving their relationships. Many researchers have proposed various mathematical models and node metrics on social networks. A network modeled as an edge-weighted directed graph is called a weighted signed network (WSN). We assume each node subjectively evaluates other nodes related to itself by scores and therefore refer to them as subjective scores. We obtain a weighted signed network by relating subjective scores to edge weights. There are many studies of methods to calculate the reputation and reliability metrics of nodes from the viewpoint of a whole network by using these subjective scores. However, subjective scores of each node tend to be confidential information for a person and organization. Nodes therefore wish to keep their scores confidential. This paper proposes exponentially convergent scores called 2-fairness and 2-goodness, for nodes of weighted signed networks and proposes a secure rating computation for them that keeps each node's subjective scores and related information secret.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); DOI http://dx.doi.org/10.2197/ipsjjip.32.710; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 65, 号 9, 発行日 2024-09-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会