Throughout the various containment phases of a pandemic, such as Covid-19, digital tools and services prove to be essential measures to counteract its disrupting effects in social and working interactions. Within this scope, Nausica@DApp, the comprehensive solution proposed in this paper, is capable of facilitating a manageable co-existence of the teaching and research activities of a campus corporation with the organizational constraints posed by the pandemic, or even when the virus will exhibit an endemic behavior, throughout several intervention areas, such as personnel contact tracing, crowd gathering surveillance, and epidemiological monitoring. These operational requirements, in particular indirect contact tracing and overcrowd monitoring, call for the adoption of an absolute device localization paradigm, which, in the proposed solution, has been devised on top of the campus WiFi infrastructure and has proved to be encouragingly accurate in most cases. Absolute localization, on the other hand, imply a certain degree of server-based centralized operations, which might affect the preservation of user data privacy. The novelty of the proposed solution consists in maximizing confidentiality and integrity in sensitive personal information in spite of the centralized localization system, by means of a mostly decentralized approach, with mobile apps performing contact tracing matching operations locally to the portable devices on pseudonymized user data, whose authenticity is guaranteed by a blockchain which preserves their cryptographic hashes. Furthermore, the proposed novel solution includes enforcing privacy preservation by avoiding the use of the Bluetooth app-to-app channel for user data exchange from the overall design, conversely a typical approach of most contract tracing solutions. By means of a sensible use of the blockchain features, integrated into the solution’s microservice-based backend, a higher degree of operation transparency to rely on boosts the user’s level of trust. As a result of using the proposed solution, the availability and reliability of data about people gathering within the campus premises are enhanced; moreover, contact tracing is achieved while using the WiFi services, hence without forcing users to adopt new habits, and without compromising privacy. The overall system has been analysed in terms of performance and costs, and the experiments have shown that its adoption is viable and effective.