  With the meteoric growth of technology, individuals and organizations are
widely adopting cloud services to mitigate the burdens of maintenance. Despite
its scalability and ease of use, many users who own sensitive data refrain from
fully utilizing cloud services due to confidentiality concerns. Maintaining
data confidentiality for data at rest and in transit has been widely explored
but data remains vulnerable in the cloud while it is in use. This vulnerability
is further elevated once the scope of computing spans across the edge-to-cloud
continuum. Accordingly, the goal of this dissertation is to enable data
confidentiality by adopting confidential computing across the continuum.
Towards this goal, one approach we explore is to separate the intelligence
aspect of data processing from the pattern-matching aspect. We present our
approach to make confidential data clustering on the cloud, and then develop
confidential search service across edge-to-cloud for unstructured text data.
Our proposed clustering solution named ClusPr, performs topic-based clustering
for static and dynamic datasets that improves cluster coherency up to
30%-to-60% when compared with other encryption-based clustering techniques. Our
trusted enterprise search service named SAED, provides context-aware and
personalized semantic search over confidential data across the continuum. We
realized that enabling confidential computing across edge-to-cloud requires
major contribution from the edge tiers particularly to run multiple Deep
Learning (DL) services concurrently. This raises memory contention on the edge
tier. To resolve this, we develop Edge-MultiAI framework to manage Neural
Network (NN) models of DL applications such that it can meet the latency
constraints of the DL applications without compromising inference accuracy.

Distributed, Parallel, and Cluster Computing;Cryptography and Security