您当前所在位置: 首页 > 学术预告
美国伊利诺伊州理工学院洪源博士特邀学术报告
  • 发布单位:计算机与网络安全学院
  • 发布时间:2018-07-05
  • 访问量:
  • 字体大小:  
  • 分享到:

主题:Preserving Privacy in the Smart Grid

 

时间:201876日(星期五)上午1040

 

地点:学术会议中心 301

 

主办单位:计算机与网络安全学院、无线智能网络实验室

 

报告人简介:

说明: E:\理工工作\万鹏俊教授\特邀专家\2018年度来访专家\7月讲座安排\20180706中国籍洪源讲师(5号-8号)\微信图片_20180622154235.jpg

     Dr. Yuan Hong is an Assistant Professor in the Department of Computer Science at Illinois Institute of Technology. Prior to joining Illinois Tech, he was an Assistant Professor in the State University of New York (SUNY) at Albany, and received his Ph.D. degree from Rutgers, the State University of New Jersey. His research interests primarily lie in the fields of privacy, security, optimization and data analytics. As such, he is very interested in resolving the security and privacy issues in both fundamental problems (e.g., optimization models) and data intensive systems (e.g., the smart grid, web search, intelligent transportation systems, and data mining). His work has been supported by the NSF.

 

报告内容摘要:

 

The smart grid integrates sensors and communication networks into the existing power grid to ubiquitously collect data from the grid for operational intelligence. However, the collection, storage and analysis of such massive amount of data may compromise the privacy of various entities on the power grid, such as energy consumers and microgrids (which refer to small segments of the grid that can locally generate and consume energy).

 

In this talk, I will present two categories of privacy preserving schemes to quantitatively measure and bound the privacy risks in two different applications in the smart grid infrastructure: (1) energy sharing among microgrids, and (2) streaming smart meter readings for consumers. Specifically, the former scheme is proposed under the theory of secure multiparty computation (SMC) to ensure secure cooperation among multiple microgrids (i.e., globally optimizing the energy sharing with their local energy) without disclosing sensitive information to each other. In the latter scheme (for streaming smart meter readings), we define a formal privacy notion to bound the risks of inferring electric appliances’ ON/OFF status at specific times from smart meter reading streams. Then, I will present efficient algorithms to convert and stream real-time meter readings with low errors while satisfying the privacy notion.

 

 

美国伊利诺伊州理工学院洪源博士特邀学术报告

2018-07-05 学术预告

主题:Preserving Privacy in the Smart Grid

 

时间:201876日(星期五)上午1040

 

地点:学术会议中心 301

 

主办单位:计算机与网络安全学院、无线智能网络实验室

 

报告人简介:

说明: E:\理工工作\万鹏俊教授\特邀专家\2018年度来访专家\7月讲座安排\20180706中国籍洪源讲师(5号-8号)\微信图片_20180622154235.jpg

     Dr. Yuan Hong is an Assistant Professor in the Department of Computer Science at Illinois Institute of Technology. Prior to joining Illinois Tech, he was an Assistant Professor in the State University of New York (SUNY) at Albany, and received his Ph.D. degree from Rutgers, the State University of New Jersey. His research interests primarily lie in the fields of privacy, security, optimization and data analytics. As such, he is very interested in resolving the security and privacy issues in both fundamental problems (e.g., optimization models) and data intensive systems (e.g., the smart grid, web search, intelligent transportation systems, and data mining). His work has been supported by the NSF.

 

报告内容摘要:

 

The smart grid integrates sensors and communication networks into the existing power grid to ubiquitously collect data from the grid for operational intelligence. However, the collection, storage and analysis of such massive amount of data may compromise the privacy of various entities on the power grid, such as energy consumers and microgrids (which refer to small segments of the grid that can locally generate and consume energy).

 

In this talk, I will present two categories of privacy preserving schemes to quantitatively measure and bound the privacy risks in two different applications in the smart grid infrastructure: (1) energy sharing among microgrids, and (2) streaming smart meter readings for consumers. Specifically, the former scheme is proposed under the theory of secure multiparty computation (SMC) to ensure secure cooperation among multiple microgrids (i.e., globally optimizing the energy sharing with their local energy) without disclosing sensitive information to each other. In the latter scheme (for streaming smart meter readings), we define a formal privacy notion to bound the risks of inferring electric appliances’ ON/OFF status at specific times from smart meter reading streams. Then, I will present efficient algorithms to convert and stream real-time meter readings with low errors while satisfying the privacy notion.

 

 

<主站链轮 <主站链轮 现金赌博,金尊娱乐,赌博牛牛网,bwin投注 <主站链轮 亚州必赢,皇冠滚球盘,在线现金赌钱,水果拉霸机
<主站链轮 <主站链轮 亚游会,东亚娱乐,亚洲娱乐场,bbin真人 <主站链轮 现金盘口,hg现金网,真金赌场网址,线上赌球技巧
<主站链轮 <主站链轮 君搏国际,hg网娱乐,皇都国际赌场,任你博官网 <主站链轮 沙龙博彩,赌博评级网,手机澳门博彩,澳门二八杠
<主站链轮 <主站链轮 官方博彩,葡京手机版,线上真人投注,博狗官方网 <主站链轮 澳门乐8,ba娱乐,新2网公司,澳门赌博棋牌