供稿、攝影：顏志軍 編輯：高超
2012年6月25日，馬薩諸塞大學(xué)洛威爾分校管理信息系統(tǒng)專業(yè)李曉白教授應(yīng)邀為管理與經(jīng)濟(jì)學(xué)院作學(xué)術(shù)報(bào)告，顏志軍教授主持會(huì)議。
本次學(xué)術(shù)報(bào)告主要介紹了“Class Restricted Clustering and Micro-Perturbation for Data Privacy ”，為參會(huì)師生提供了數(shù)據(jù)挖掘及隱私保護(hù)研究的新思路。李曉白教授首先對(duì)自己的學(xué)術(shù)研究和歷程進(jìn)行了簡(jiǎn)單介紹，充分拉近與師生們的距離，然后用通俗易懂的語(yǔ)言介紹了其專業(yè)領(lǐng)域知識(shí)。李曉白教授指出，數(shù)據(jù)挖掘及分享技術(shù)能使企業(yè)從數(shù)據(jù)中得到有效信息，從而為顧客提供更優(yōu)質(zhì)的服務(wù)，保持良好的競(jìng)爭(zhēng)優(yōu)勢(shì)。但是隨著人們對(duì)隱私保護(hù)的重視，如何權(quán)衡數(shù)據(jù)挖掘的力度與隱私保護(hù)成為當(dāng)前研究關(guān)注的重點(diǎn)。報(bào)告過(guò)程中，李曉白教授詳細(xì)介紹了一種創(chuàng)新的方法，該方法主要利用最小生成樹(shù)技術(shù)，將實(shí)驗(yàn)的數(shù)據(jù)重新進(jìn)行分組，使得分組后的數(shù)據(jù)仍然具有整體數(shù)據(jù)的比例特征，但不會(huì)因其處于某一分組之中而暴露自己的隱私特征。同時(shí)，該方法向數(shù)據(jù)中加入了一些噪聲，中和了數(shù)據(jù)因分組導(dǎo)致的方差降低的問(wèn)題，使得人們既能知曉所需信息，又降低了隱私信息暴露的風(fēng)險(xiǎn)。最后，偉德國(guó)際官網(wǎng)師生和同學(xué)還與李曉白教授就最小生成樹(shù)技術(shù)、噪聲加入方法和隱私保護(hù)程度的測(cè)量等問(wèn)題展開(kāi)了熱烈的討論和交流。
李曉白教授新穎的學(xué)術(shù)觀點(diǎn)和嚴(yán)謹(jǐn)?shù)闹螌W(xué)理念，給與會(huì)老師以深刻的啟迪，引起大家的共鳴和思考。會(huì)后，各位師生與李曉白教授進(jìn)行了深入交流。
主講人簡(jiǎn)介：
Dr. Xiaobai Li is a Professor of Management Information Systems in the Department of Operations and Information Systems, Manning School of Business at the University of Massachusetts Lowell, USA. He received his Ph.D. in management science from the University of South Carolina in 1999. Dr. Li’s research focuses on data mining, information privacy, and information economics. He has received funding for his research from National Institutes of Health (NIH) and National Science Foundation (NSF), USA. His work has appeared or is forthcoming in Information Systems Research, Management Science, Operations Research, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Automatic Control, Communications of the ACM, Decision Support Systems, INFORMS Journal on Computing, European Journal of Operational Research, among others.
（審核：顏志軍）
