  Data privacy is unarguably of extreme importance. Nonetheless, there exist
various daunting challenges to safe-guarding data privacy. These challenges
stem from the fact that data owners have little control over their data once it
has transgressed their local storage and been managed by third parties whose
trustworthiness is questionable at times. Our work seeks to enhance data
privacy by constructing a self-expiring data capsule. Sensitive data is
encapsulated into a capsule which is associated with an access policy an
expiring condition. The former indicates eligibility of functions that can
access the data, and the latter dictates when the data should become
inaccessible to anyone, including the previously eligible functions. Access to
the data capsule, as well as its dismantling once the expiring condition is
met, are governed by a committee of independent and mutually distrusting nodes.
The pivotal contribution of our work is an integration of hardware primitive,
state machine replication and threshold secret sharing in the design of the
self-expiring data encapsulation framework. We implement the proposed framework
in a system called TEEKAP. Our empirical experiments conducted on a realistic
deployment setting with the access control committee spanning across four
geographical regions reveal that TEEKAP can process access requests at scale
with sub-second latency.
