  In a Public Safety (PS) situation, agents may require critical and personally
identifiable information. Therefore, not only does context and location-aware
information need to be available, but also the privacy of such information
should be preserved. Existing solutions do not address such a problem in a PS
environment. This paper proposes a framework in which anonymized Personal
Information (PI) is accessible to authorized public safety agents under a PS
circumstance. In particular, we propose a secure data storage structure along
with privacy-preserving mobile search framework, suitable for Public Safety
Networks (PSNs). As a result, availability and privacy of PI are achieved
simultaneously. However, the design of such a framework encounters substantial
challenges, including scalability, reliability of the data, computation and
communication and storage efficiency, etc. We leverage Secure Indexing (SI)
methods and modify Bloom Filters (BFs) to create a secure data storage
structure to store encrypted meta-data. As a result, our construction enables
secure and privacy-preserving multi-keyword search capability. In addition, our
system scales very well, maintains availability of data, imposes minimum delay,
and has affordable storage overhead. We provide extensive security analysis,
simulation studies, and performance comparison with the state-of-the-art
solutions to demonstrate the efficiency and effectiveness of the proposed
approach. To the best of our knowledge, this work is the first to address such
issues in the context of PSNs.
