  This paper proposes Yggdrasil, a protocol for privacy-aware dual data
deduplication in multi client settings. Yggdrasil is designed to reduce the
cloud storage space while safeguarding the privacy of the client's outsourced
data. Yggdrasil combines three innovative tools to achieve this goal. First,
generalized deduplication, an emerging technique to reduce data footprint.
Second, non-deterministic transformations that are described compactly and
improve the degree of data compression in the Cloud (across users). Third, data
preprocessing in the clients in the form of lightweight, privacy-driven
transformations prior to upload. This guarantees that an honest-but-curious
Cloud service trying to retrieve the client's actual data will face a high
degree of uncertainty as to what the original data is. We provide a
mathematical analysis of the measure of uncertainty as well as the compression
potential of our protocol. Our experiments with a HDFS log data set shows that
49% overall compression can be achieved, with clients storing only 12% for
privacy and the Cloud storing the rest. This is achieved while ensuring that
each fragment uploaded to the Cloud would have 10^296 possible original strings
from the client. Higher uncertainty is possible, with some reduction of
compression potential.
