scholarly journals Acceptable Surveillance-Orientated Security Technologies: Insights from the SurPRISE Project

2015 ◽  
Vol 13 (3/4) ◽  
pp. 437-454 ◽  
Author(s):  
Sara Degli Esposti ◽  
Elvira Santiago-Gomez

Pre-emptive security emphasizes the necessity of envisioning and designing technologies enabling the anticipation and management of emergent risks threatening human and public security. Surveillance functionalities are embedded in the design of these technologies to allow constant monitoring, preparedness and prevention. Yet surveillance-orientated security technologies, such as smart CCTVs or Deep Packet Inspection, bring along with their implementation other risks, such as risks of privacy infringement, discrimination, misuse, abuse, or errors, which have often triggered public outrage and resistance. The same measures meant to foster human security, can potentially make people feel insecure, vulnerable, and exposed. This outcome is the result of a narrow approach toward problem solving that does not take into account those same people the technology is supposed to protect. Drawing from both the socio-cultural and psychometric approaches to risk analysis and from the literature on public engagement in science and technology, this article presents a new methodological tool, which combines traditional citizen summit method with an innovative mixed-method research design. The objective of this new form of participatory exercise is to engage the public and gather socially robust and in context knowledge about the public acceptability of these technologies. The method has been developed as part of the SurPRISE project, funded by the European Commission under the SeventhFramework Program. The article presents the theoretical framework and preliminary results of citizen summits organized across Europe.

2009 ◽  
Vol 20 (8) ◽  
pp. 2214-2226 ◽  
Author(s):  
Qian XU ◽  
Yue-Peng E ◽  
Jing-Guo GE ◽  
Hua-Lin QIAN

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1376
Author(s):  
Yung-Fa Huang ◽  
Chuan-Bi Lin ◽  
Chien-Min Chung ◽  
Ching-Mu Chen

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.


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