Cloud computing has become a standard computational paradigm due its numerous
advantages, including high availability, elasticity, and ubiquity. Both individual users and
companies are adopting more of its services, but not without loss of privacy and control.
Outsourcing data and computations to a remote server implies trusting its owners, a
problem many end-users are aware. Recent news have proven data stored on Cloud
servers is susceptible to leaks from the provider, third-party attackers, or even from
government surveillance programs, exposing users’ private data.
Different approaches to tackle these problems have surfaced throughout the years.
Naïve solutions involve storing data encrypted on the server, decrypting it only on the
client-side. Yet, this imposes a high overhead on the client, rendering such schemes
impractical. Searchable Symmetric Encryption (SSE) has emerged as a novel research
topic in recent years, allowing efficient querying and updating over encrypted datastores
in Cloud servers, while retaining privacy guarantees. Still, despite relevant recent advances,
existing SSE schemes still make a critical trade-off between efficiency, security,
and query expressiveness, thus limiting their adoption as a viable technology, particularly
in large-scale scenarios.
New technologies providing Isolated Execution Environments (IEEs) may help improve
SSE literature. These technologies allow applications to be run remotely with
privacy guarantees, in isolation from other, possibly privileged, processes inside the CPU,
such as the operating system kernel. Prominent example technologies are Intel SGX and
ARM TrustZone, which are being made available in today’s commodity CPUs.
In this thesis we study these new trusted hardware technologies in depth, while exploring
their application to the problem of searching over encrypted data, primarily focusing
in SGX. In more detail, we study the application of IEEs in SSE schemes, improving their
efficiency, security, and query expressiveness.
We design, implement, and evaluate three new SSE schemes for different query types,
namely Boolean queries over text, similarity queries over image datastores, and multimodal
queries over text and images. These schemes can support queries combining different
media formats simultaneously, envisaging applications such as privacy-enhanced medical diagnosis and management of electronic-healthcare records, or confidential photograph
catalogues, running without the danger of privacy breaks in Cloud-based provisioned
services
