**Acknowledgements**

*3.2.3 Data protection, legal compliance and ethics*

*Deep Learning Applications*

Security and safety management systems and their data fusion and intelligent analytics capabilities require substantial data collection and processing in order to offer the best possible awareness and decision support to C&C operators, field personnel and first responders. Especially in the context of homeland security, privacy and data protection is often seen through the typical trade-off model perspective, requesting the public to give up –in the best case knowingly- on particular rights over the control of their personal data. However, such systems should not be based and developed on exceptions or operate only in extraordinary circumstances, the latter being very inefficient. With the latest guidelines of EU General Data Protection Regulation (GDPR), principles of data minimization and privacy by

design will shift from best practices into a much more regulated form.

calculated on how a person acts on the scene and not any discriminatory

A subject of past and current research, assessing the societal acceptance of surveillance and security solutions comes with its own challenges. Acceptance is based on multiple parameters, individual perceptions and sometimes misconceptions and individual practices which may not be in line with the expressed concerns [24]. The proposed system and the overall risk-based security paradigm, is based on the positive fact that the vast majority of people have no malicious intent. The system focuses on the unknown and high-risk cases, intending to shift the current practices from annoying horizontal and disruptive processes to seamless and unobtrusive security. The combination of privacy and ethics by design along with the ethical and unobtrusive treatment set the parameters for a system with high

In this chapter we discussed the concept of risk-based security, the possible trade-off between increased convenience for passengers from risk-based security

and the delays induced by additional checks needed for establishing each

background information.

**4. Conclusions**

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acceptance, positive public perception and trust.

The proposed system is in line with these principles, following a "by design approach" in terms of data protection and ethics. Data collected are structurally separated from identifiable information, and identification occurs only upon the logged and explicit intervention of a human operator when truly needed. By assessing risks on real time, the system itself has the advantage of performing data minimization through early elimination of lower risk cases. On the front end and field, privacy enhancing technologies and smart sensors are also preferred and selected. E.g. smart visual sensors with on-board processing capabilities can filter out data before sending it over the wire and to the server for processing. Moreover, the system has been designed to include specific safeguards to protect individuals against discrimination, stigmatization and unduly prohibition of access to goods and services. Defined in [23], the system adopts these definitions and extends them to all protected grounds as defined in the Charter and the Treaty of Amsterdam, taking also into account the proposal for the horizontal directive that extends the context of EU non-discrimination law and prohibits discrimination "on grounds of sex, racial or ethnic origin, age, disability, sexual orientation, religion or belief". In this context, Fairness and bias detection algorithms are applied to the adaptive learning management system while the human operator remains in control of the final enforcement following any automated decision making process. Intelligent behavior analytics can further support the case where security risks are based and

The research described in this paper has been supported by the following research contracts:

"**FLYSEC**: Optimizing time-to-FLY and enhancing airport SECurity," Programme: Horizon 2020, European Union Grant Agreement No. 653879, Duration: 01/05/2015 - 31/07/2018, http://www.fly-sec.eu.

"**TRESSPASS**: Robust Risk Based Screening and Alert System for Travelers and luggage," Grant Agreement No. 787120, Call: H2020-SEC-2016-2017-2, https:// www.tresspass.eu/The-project.

The author would also like to acknowledge the use of some material from the Refs. [5–10, 12, 14]. He co-authored in collaboration with his colleagues whose names appear in these references.
