  Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing
paradigm due to its cost-effectiveness. This approach relies on platforms to
outsource tasks to participating workers when prompted by task publishers.
Although incentive mechanisms have been devised to foster widespread
participation in MCS, most of them focus only on static tasks (i.e., tasks for
which the timing and type are known in advance) and do not protect the privacy
of worker bids. In a dynamic and resource-constrained environment, tasks are
often uncertain (i.e., the platform lacks a priori knowledge about the tasks)
and worker bids may be vulnerable to inference attacks. This paper presents
HERALD*, an incentive mechanism that addresses these issues through the use of
uncertainty and hidden bids. Theoretical analysis reveals that HERALD*
satisfies a range of critical criteria, including truthfulness, individual
rationality, differential privacy, low computational complexity, and low social
cost. These properties are then corroborated through a series of evaluations.
