Access control policies describe high level requirements for access control systems. Access control rule sets ideally translate these policies into a coherent and manageable collection of Allow/Deny rules. Designing rule sets that reflect desired policies is a difficult and time-consuming task. The result is that rule sets are difficult to understand and manage. The goal of this paper is to provide means for obtaining usable access control rule sets, which we define as rule sets that (i) reflect the access control policy and (ii) are easy to understand and manage. In this paper, we formally define the challenges that users face when generating usable access control rule sets and provide formal tools to handle them more easily. We started our research with a pilot study in which specialists were interviewed. The objective was to list usability challenges regarding the management of access control rule sets and verify how those challenges were handled by specialists. The results of the pilot study were compared and combined with results from related work and refined into six novel, formally defined metrics that are used to measure the security and usability aspects of access control rule sets. We validated our findings with two user studies, which demonstrate that our metrics help users generate statistically significant better rule sets.
Accurate and trusted identifiers are a centerpiece for any security architecture. Protecting against Sybil attacks in a privacy-friendly manner is a non-trivial problem in wireless infrastructureless networks, such as mobile ad hoc networks. In this paper, we introduce self-certified Sybil-free pseudonyms as a means to provide privacy-friendly Sybil-freeness without requiring continuous online availability of a trusted third party. These pseudonyms are self-certified and computed by the users themselves from their cryptographic longterm identities. Contrary to identity certificates, we preserve location privacy and improve protection against some notorious attacks on anonymous communication systems.
Background Community-based primary care focuses on health promotion, awareness raising, and illnesses treatment and prevention in individuals, groups, and communities. Community Health Workers (CHWs) are the leading actors in such programs, helping to bridge the gap between the population and the health system. Many mobile health (mHealth) initiatives have been undertaken to empower CHWs and improve the data collection process in the primary care, replacing archaic paper-based approaches. A special category of mHealth apps, known as mHealth Data Collection Systems (MDCSs), is often used for such tasks. These systems process highly sensitive personal health data of entire communities so that a careful consideration about privacy is paramount for any successful deployment. However, the mHealth literature still lacks methodologically rigorous analyses for privacy and data protection. Objective In this paper, a Privacy Impact Assessment (PIA) for MDCSs is presented, providing a systematic identification and evaluation of potential privacy risks, particularly emphasizing controls and mitigation strategies to handle negative privacy impacts. Methods The privacy analysis follows a systematic methodology for PIAs. As a case study, we adopt the GeoHealth system, a large-scale MDCS used by CHWs in the Family Health Strategy, the Brazilian program for delivering community-based primary care. All the PIA steps were taken on the basis of discussions among the researchers (privacy and security experts). The identification of threats and controls was decided particularly on the basis of literature reviews and working group meetings among the group. Moreover, we also received feedback from specialists in primary care and software developers of other similar MDCSs in Brazil. Results The GeoHealth PIA is based on 8 Privacy Principles and 26 Privacy Targets derived from the European General Data Protection Regulation. Associated with that, 22 threat groups with a total of 97 subthreats and 41 recommended controls were identified. Among the main findings, we observed that privacy principles can be enhanced on existing MDCSs with controls for managing consent, transparency, intervenability, and data minimization. Conclusions Although there has been significant research that deals with data security issues, attention to privacy in its multiple dimensions is still lacking for MDCSs in general. New systems have the opportunity to incorporate privacy and data protection by design. Existing systems will have to address their privacy issues to comply with new and upcoming data protection regulations. However, further research is still needed to identify feasible and cost-effective solutions.
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