TY - GEN
T1 - Cloning your mind
T2 - 52nd ACM/EDAC/IEEE Design Automation Conference, DAC 2015
AU - Liu, Beiye
AU - Wu, Chunpeng
AU - Li, Hai
AU - Chen, Yiran
AU - Wu, Qing
AU - Barnell, Mark
AU - Qiu, Qinru
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/7/24
Y1 - 2015/7/24
N2 - With the booming of big-data applications, cognitive information processing systems that leverage advanced data processing technologies, e.g., machine learning and data mining, are widely used in many industry fields. Although these technologies demonstrate great processing capability and accuracy in the relevant applications, several security and safety challenges are also emerging against these learning based technologies. In this paper, we will first introduce several security concerns in cognitive system designs. Some real examples are then used to demonstrate how the attackers can potentially access the confidential user data, replicate a sensitive data processing model without being granted the access to the details of the model, and obtain some key features of the training data by using the services publically accessible to a normal user. Based on the analysis of these security challenges, we also discuss several possible solutions that can protect the information privacy and security of cognitive systems during different stages of the usage.
AB - With the booming of big-data applications, cognitive information processing systems that leverage advanced data processing technologies, e.g., machine learning and data mining, are widely used in many industry fields. Although these technologies demonstrate great processing capability and accuracy in the relevant applications, several security and safety challenges are also emerging against these learning based technologies. In this paper, we will first introduce several security concerns in cognitive system designs. Some real examples are then used to demonstrate how the attackers can potentially access the confidential user data, replicate a sensitive data processing model without being granted the access to the details of the model, and obtain some key features of the training data by using the services publically accessible to a normal user. Based on the analysis of these security challenges, we also discuss several possible solutions that can protect the information privacy and security of cognitive systems during different stages of the usage.
KW - Cognitive Systems
KW - Machine Learning
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=84944080722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944080722&partnerID=8YFLogxK
U2 - 10.1145/2744769.2747915
DO - 10.1145/2744769.2747915
M3 - Conference contribution
AN - SCOPUS:84944080722
T3 - Proceedings - Design Automation Conference
BT - 2015 52nd ACM/EDAC/IEEE Design Automation Conference, DAC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 June 2015 through 12 June 2015
ER -