一種基于私有區(qū)塊鏈的信息保護預測模型研究
發(fā)布時間：2018-08-07 13:15
【摘要】：[目的/意義]預測建模是數(shù)據(jù)挖掘的基礎任務之一,當前基于信息保護的預測模型大多建立在一個中心化的架構之下,因而不可避免地存在一些安全性和魯棒性漏洞。因此嘗試一種新的、去中心化的預測建模方法,能夠兼具敏感信息保護和數(shù)據(jù)處理能力,無論對跨機構互操作還是對國家層面的信息安全與共享服務,均有重要的意義。[方法/過程]文章提出了一個新的預測模型架構——模型鏈,將基于信息保護的在線機器學習與私有區(qū)塊鏈網(wǎng)絡技術相互整合,應用交易元數(shù)據(jù)傳遞局部模型,并提出一種新的信息證明算法以確定在線學習進程的執(zhí)行順序。[結果/結論]在模型鏈中,每個參與的站點都將有助于模型參數(shù)估計,但無需透露己方的任何信息(即只有模型參數(shù)而沒有用戶數(shù)據(jù)在機構間進行交換)�；趨^(qū)塊鏈技術的預測建模能夠有效提高機構間互操作性,同時規(guī)避了敏感信息泄露、數(shù)據(jù)處理需要中央服務器引導而產(chǎn)生的種種系統(tǒng)性、政策性風險。該方法將有助于支持全國范圍內的信息共享服務的路線圖設計。[局限]模型鏈的技術實施還有待結合具體的網(wǎng)絡環(huán)境進行評估,這也是開展后續(xù)研究的目標。
[Abstract]:[objective / significance] Prediction modeling is one of the basic tasks of data mining. Currently, most of the prediction models based on information protection are based on a centralized framework, so there are inevitably some security and robustness vulnerabilities. Therefore, it is of great significance to try a new, decentralized predictive modeling method with both sensitive information protection and data processing capabilities, both for cross-agency interoperability and for information security and sharing services at the national level. [method / process] this paper proposes a new predictive model architecture, model chain, which integrates online machine learning based on information protection with private block chain network technology, and applies transaction metadata to transfer local model. A new information proof algorithm is proposed to determine the execution order of the online learning process. [results / conclusions] in the model chain, each participating site will contribute to the estimation of model parameters, but there is no need to disclose any information about the model (that is, only model parameters and no user data are exchanged between agencies). The prediction modeling based on block chain technology can effectively improve the interoperability between institutions and avoid the systematic and policy risks caused by the disclosure of sensitive information and the need for the central server to guide data processing. This approach will help support the roadmap design of information-sharing services across the country. The technical implementation of the model chain needs to be evaluated in the light of the specific network environment, which is also the goal of further research.
【作者單位】： 武漢大學信息管理學院;武漢大學計算機學院;
【基金】：國家自然科學基金項目“融合情境的移動閱讀推薦系統(tǒng)研究”(項目編號:71373192),國家自然科學基金項目“面向移動終端的情境隱私保護理論與方法研究”(項目編號:61572380)和國家自然科學基金面上項目“無線Mesh網(wǎng)絡中跨層安全關鍵技術研究”(項目編號:61272451)的部分成果
【分類號】：G203
本文編號：2170136
[Abstract]:[objective / significance] Prediction modeling is one of the basic tasks of data mining. Currently, most of the prediction models based on information protection are based on a centralized framework, so there are inevitably some security and robustness vulnerabilities. Therefore, it is of great significance to try a new, decentralized predictive modeling method with both sensitive information protection and data processing capabilities, both for cross-agency interoperability and for information security and sharing services at the national level. [method / process] this paper proposes a new predictive model architecture, model chain, which integrates online machine learning based on information protection with private block chain network technology, and applies transaction metadata to transfer local model. A new information proof algorithm is proposed to determine the execution order of the online learning process. [results / conclusions] in the model chain, each participating site will contribute to the estimation of model parameters, but there is no need to disclose any information about the model (that is, only model parameters and no user data are exchanged between agencies). The prediction modeling based on block chain technology can effectively improve the interoperability between institutions and avoid the systematic and policy risks caused by the disclosure of sensitive information and the need for the central server to guide data processing. This approach will help support the roadmap design of information-sharing services across the country. The technical implementation of the model chain needs to be evaluated in the light of the specific network environment, which is also the goal of further research.
【作者單位】： 武漢大學信息管理學院;武漢大學計算機學院;
【基金】：國家自然科學基金項目“融合情境的移動閱讀推薦系統(tǒng)研究”(項目編號:71373192),國家自然科學基金項目“面向移動終端的情境隱私保護理論與方法研究”(項目編號:61572380)和國家自然科學基金面上項目“無線Mesh網(wǎng)絡中跨層安全關鍵技術研究”(項目編號:61272451)的部分成果
【分類號】：G203
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