International Journal of Information Security, Springer, In Press. DOI (I.F.: 1.494)
Nowadays, smart home devices like Amazon Echo and Google Home have reached mainstream popularity.
Being in the homes of users, these devices are intrinsically intrusive, being able to access details such as users' name, gender, home address, calendar appointments and others.
There are growing concerns about indiscriminate data collection and invasion of user privacy in smart home devices, but studies show that perceived benefits are exceeding perceived risks when it comes to consumers.
As a result, consumers are placing a lot of trust in these devices, sometimes without realizing it.
Improper trust assumptions and security controls can lead to unauthorized access and control of the devices, which can result in serious consequences.
In this paper, we explore the behaviour of devices such as Amazon Echo and Google Home in a smart home setting with respect to trust relationships and propose a trust model to improve these relationships among all the involved actors.
We have evaluated how trust was built and managed from the initial set up phase to the normal operation phase, during which we performed a number of interaction tests with different types of users (i.e. owner, guests).
As a result, we were able to assess the effectiveness of the provided security controls and identify potential relevant security issues. In order to address the identified issues, we defined a trust model and propose a solution based on it for further securing smart home systems.
Computers & Security, vol. 77 , issue August 2018, Elsevier, pp. 773-789, 2018. DOI (I.F.: 3.062)
Trust negotiations are mechanisms that enable interaction between previously unknown users. After exchanging various pieces of potentially sensitive information, the participants of a negotiation can decide whether or not to trust one another. Therefore, trust negotiations bring about threats to personal privacy if not carefully considered. This paper presents a framework for representing trust negotiations in the early phases of the Software Development Life Cycle (SDLC). The framework can help software engineers to determine the most suitable policies for the system by detecting conflicts between privacy and trust requirements. More precisely, we extend the SI* modelling language and provide a set of predicates for defining trust and privacy policies and a set of rules for describing the dynamics of the system based on the established policies. The formal representation of the model facilitates its automatic verification. The framework has been validated in a distributed social network scenario for connecting drivers with potential passengers willing to share a journey.
12th International Workshop on Security and Trust Management (STM), vol. LNCS 9871, Springer, pp. 98-105, 09/2016. DOI
Software engineering and information security have traditionally followed divergent paths but lately some efforts have been made to consider security from the early phases of the Software Development Life Cycle (SDLC). This paper follows this line and concentrates on the incorporation of trust negotiations during the requirements engineering phase. More precisely, we provide an extension to the SI* modelling language, which is further formalised using answer set programming specifications to support the automatic verification of the model and the detection of privacy conflicts caused by trust negotiations.
Accountability and Security in the Cloud, M. Felici, and C. Fernandez-Gago Eds., Lecture Notes in Computer Science 8937, Springer International Publishing, pp. 114-125, 2015. DOI
In this paper we tackle the problem of privacy and confidentiality in Identity Management as a Service (IDaaS). The adoption of cloud computing technologies by organizations has fostered the externalization of the identity management processes, shaping the concept of Identity Management as a Service. However, as it has happened to other cloud-based services, the cloud poses serious risks to the users, since they lose the control over their data. As part of this work, we analyze these concerns and present a model for privacy-preserving IDaaS, called BlindIdM, which is designed to provide data privacy protection through the use of cryptographic safeguards.