@article {Boughrous2022321,
	title = {The Pandemic Impact on Organizations Security and Resiliency: The Workflow Satisfiability Problem},
	journal = {Lecture Notes in Networks and Systems},
	volume = {420 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {321-329},
	abstract = {The new work model imposed during the pandemic impacted organizations badly, especially those with rigid business strategies. Therefore, they were forced to provide digital platforms for their clients and allow remote work for employees. The rapid shift to remote work increased cyberthreats and attack vectors. Thus, organizations had to enforce their security policy, apply security constraints for a safe and secure inter-organizational collaborations. Consequently, the absence of employees because of the pandemic and the rigid application of the security policy caused the unavailability of collaborating organizations. Thus, the execution of inter-organizational workflow may not be complete, which increases the possibility that the workflow satisfiability problem (WSP) occurs. In this paper, we present an approach to selecting an inter-organizational workflow assignment plan. It uses the multicriteria decision method and Bayesian inference to select organizations for collaboration. The approach aims to ensure resilient and secure inter-organizational workflow execution with a low probability for the WSP. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-96305-7_30},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126376866\&doi=10.1007\%2f978-3-030-96305-7_30\&partnerID=40\&md5=15bd75caca2d063eff65da1b12210dba},
	author = {Boughrous, M. and El Bakkali, H. and El Kandoussi, A.}
}
@book {ElOuazzani20211503,
	title = {Privacy Preserving in Digital Health: Main Issues, Technologies, and Solutions},
	series = {Research Anthology on Privatizing and Securing Data},
	year = {2021},
	note = {cited By 1},
	pages = {1503-1526},
	abstract = {Recently, digital health solutions are taking advantage of recent advances in information and communication technologies. In this context, patients{\textquoteright} health data are shared with other stakeholders. Moreover, it{\textquoteright}s now easier to collect massive health data due to the rising use of connected sensors in the health sector. However, the sensitivity of this shared healthcare data related to patients may increase the risks of privacy violation. Therefore, healthcare-related data need robust security measurements to prevent its disclosure and preserve patients{\textquoteright} privacy. However, in order to make well-informed decisions, it is often necessary to allow more permissive security policies for healthcare organizations even without the consent of patients or against their preferences. The authors of this chapter concentrate on highlighting these challenging issues related to patient privacy and presenting some of the most significant privacy preserving approaches in the context of digital health. {\textcopyright} 2021, IGI Global.},
	doi = {10.4018/978-1-7998-8954-0.ch072},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127530934\&doi=10.4018\%2f978-1-7998-8954-0.ch072\&partnerID=40\&md5=79d0013c567793bf90816fc766b5c8c2},
	author = {El Ouazzani, Z. and El Bakkali, H. and Sadki, S.}
}
@article {ElOuazzani2021,
	title = {Proximity Measurement for Hierarchical Categorical Attributes in Big Data},
	journal = {Security and Communication Networks},
	volume = {2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Nearly most of the organizations store massive amounts of data in large databases for research, statistics, and mining purposes. In most cases, much of the accumulated data contain sensitive information belonging to individuals which may breach privacy. Hence, ensuring privacy in big data is considered a very important issue. The concept of privacy aims to protect sensitive information from various attacks that may violate the identity of individuals. Anonymization techniques are considered the best way to ensure privacy in big data. Various works have been already realized, taking into account horizontal clustering. The L-diversity technique is one of those techniques dealing with sensitive numerical and categorical attributes. However, the majority of anonymization techniques using L-diversity principle for hierarchical data cannot resist the similarity attack and therefore cannot ensure privacy carefully. In order to prevent the similarity attack while preserving data utility, a hybrid technique dealing with categorical attributes is proposed in this paper. Furthermore, we highlighted all the steps of our proposed algorithm with detailed comments. Moreover, the algorithm is implemented and evaluated according to a well-known information loss-based criterion which is Normalized Certainty Penalty (NCP). The obtained results show a good balance between privacy and data utility. {\textcopyright} 2021 Zakariae El Ouazzani et al.},
	keywords = {Big data, Categorical attributes, Data utilities, Hierarchical data, Hybrid techniques, Information loss, Large database, Privacy by design, Sensitive informations, Various attacks},
	doi = {10.1155/2021/6612923},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111105073\&doi=10.1155\%2f2021\%2f6612923\&partnerID=40\&md5=a1855aa8b73f4bcdebad1b181b25c7ac},
	author = {El Ouazzani, Z. and Braeken, A. and El Bakkali, H.}
}
@article {ElOuazzani2019125,
	title = {Privacy in big data through variable t-closeness for MSN attributes},
	journal = {Lecture Notes in Networks and Systems},
	volume = {49},
	year = {2019},
	note = {cited By 0},
	pages = {125-141},
	doi = {10.1007/978-3-319-97719-5_9},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063295369\&doi=10.1007\%2f978-3-319-97719-5_9\&partnerID=40\&md5=04a9a65601ac043a62f9401a6b64015f},
	author = {El Ouazzani, Z. and El Bakkali, H.}
}
@conference {ElKalam2012,
	title = {Preface [Preface]},
	booktitle = {Proceedings of the 2nd National Days of Network Security and Systems, JNS2 2012},
	year = {2012},
	note = {cited By 0},
	doi = {10.1109/JNS2.2012.6249227},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866688620\&doi=10.1109\%2fJNS2.2012.6249227\&partnerID=40\&md5=7fd94ebf5742b98414b8aca656a1701c},
	author = {El Kalam, A.A. and El Bakkali, H. and Ouadghiri, D.}
}
