In a Public Safety (PS) situation, agents may require critical and personallyidentifiable information. Therefore, not only does context and location-awareinformation need to be available, but also the privacy of such informationshould be preserved. Existing solutions do not address such a problem in a PSenvironment. This paper proposes a framework in which anonymized PersonalInformation (PI) is accessible to authorized public safety agents under a PScircumstance. In particular, we propose a secure data storage structure alongwith privacy-preserving mobile search framework, suitable for Public SafetyNetworks (PSNs). As a result, availability and privacy of PI are achievedsimultaneously. However, the design of such a framework encounters substantialchallenges, including scalability, reliability of the data, computation andcommunication and storage efficiency, etc. We leverage Secure Indexing (SI)methods and modify Bloom Filters (BFs) to create a secure data storagestructure to store encrypted meta-data. As a result, our construction enablessecure and privacy-preserving multi-keyword search capability. In addition, oursystem 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-artsolutions to demonstrate the efficiency and effectiveness of the proposedapproach. To the best of our knowledge, this work is the first to address suchissues in the context of PSNs.
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