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Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service.
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Privacy Leakage in Discrete-Time Updating Systems
A source generates time-stamped update packets that are sent to a server and then forwarded to a monitor. This occurs in the presence of an adversary that can infer information about the source by observing the output process of the server. The server wishes to release updates in a timely way to the monitor but also wishes to minimize the information leaked to the adversary. We analyze the trade-off between the age of information (AoI) and the maximal leakage for systems in which the source generates updates as a Bernoulli process. For a time slotted system in which sending an update requires one slot, we consider three server policies: (1) Memoryless with Bernoulli Thinning (MBT): arriving updates are queued with some probability and head-of-line update is released after a geometric holding time; (2) Deterministic Accumulate-and-Dump (DAD): the most recently generated update (if any) is released after a fixed time; (3) Random Accumulate-and-Dump (RAD): the most recently generated update (if any) is released after a geometric waiting time. We show that for the same maximal leakage rate, the DAD policy achieves lower age compared to the other two policies but is restricted to discrete age-leakage operating points.
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- PAR ID:
- 10389923
- Date Published:
- Journal Name:
- 2022 IEEE International Symposium on Information Theory (ISIT)
- Page Range / eLocation ID:
- 2076 to 2081
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISIT50566.2022.9834673
