Summary of Pearl: Personalized Privacy Of Human-centric Systems Using Early-exit Reinforcement Learning, by Mojtaba Taherisadr et al.
PEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning
by Mojtaba Taherisadr, Salma Elmalaki
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces PEaRL, a system that adapts its approach to individual behavioral patterns and preferences to enhance privacy preservation in human-centric systems. PEaRL employs reinforcement learning (RL) for adaptability and an early-exit strategy that balances privacy protection and system utility. The system is evaluated in two contexts: Smart Home environments and Virtual Reality (VR) Smart Classrooms, demonstrating its ability to provide a personalized tradeoff between user privacy and application utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PEaRL is a new way to keep people’s information private when they’re using smart homes or virtual reality classrooms. Right now, some systems try to keep things private but don’t work well because people act differently all the time. PEaRL uses special learning techniques and rules to figure out what each person wants and makes sure their privacy is protected while still letting them use the system. |
Keywords
* Artificial intelligence * Reinforcement learning
