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The number of smart home IoT (Internet of Things) devices has been growing fast in recent years. Along with the great benefits brought by smart home devices, new threats have appeared. One major threat to smart home users is the compromise of their privacy by traffic analysis (TA) attacks. Researchers have shown that TA attacks can be performed successfully on either plain or encrypted traffic to identify smart home devices and infer user activities. Tunneling traffic is a very strong countermeasure to existing TA attacks. However, in this work, we design a Signature based Tunneled Traffic Analysis (STTA) attack that can be effective even on tunneled traffic. Using a popular smart home traffic dataset, we demonstrate that our attack can achieve an 83% accuracy on identifying 14 smart home devices. We further design a simple defense mechanism based on adding uniform random noise to effectively protect against our TA attack without introducing too much overhead. We prove that our defense mechanism achieves approximate differential privacy.
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Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection
There are a number of forums where people participate under pseudonyms. One example is peer review, where the identity of reviewers for any paper is confidential. When participating in these forums, people frequently engage in batching: executing multiple related tasks (e.g., commenting on multiple papers) at nearly the same time. Our empirical analysis shows that batching is common in two applications we consider -- peer review and Wikipedia edits. In this paper, we identify and address the risk of deanonymization arising from linking batched tasks. To protect against linkage attacks, we take the approach of adding delay to the posting time of batched tasks. We first show that under some natural assumptions, no delay mechanism can provide a meaningful differential privacy guarantee. We therefore propose a one-sided formulation of differential privacy for protecting against linkage attacks. We design a mechanism that adds zero-inflated uniform delay to events and show it can preserve privacy. We prove that this noise distribution is in fact optimal in minimizing expected delay among mechanisms adding independent noise to each event, thereby establishing the Pareto frontier of the trade-off between the expected delay for batched and unbatched events. Finally, we conduct a series of experiments on Wikipedia and Bitcoin data that corroborate the practical utility of our algorithm in obfuscating batching without introducing onerous delay to a system.
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- PAR ID:
- 10602127
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Measurement and Analysis of Computing Systems
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2476-1249
- Format(s):
- Medium: X Size: p. 1-41
- Size(s):
- p. 1-41
- Sponsoring Org:
- National Science Foundation
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