Data privacy in trigger-action systems
Paper i proceeding, 2021

Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users create trigger-compute-action rules that pass data between disparate Internet services. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to sensitive data for millions of users. To avoid this risk, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users' private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, it supports Boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all publicly-available rules on Zapier. Based on ten existing rules that exercise a wide variety of operations, we show that eTAP has a modest performance impact: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%.


Yunang Chen

University of Wisconsin Madison

Amrita Roy Chowdhury

University of Wisconsin Madison

Ruizhe Wang

University of Wisconsin Madison

Andrei Sabelfeld

Chalmers, Data- och informationsteknik, Informationssäkerhet

Rahul Chatterjee

University of Wisconsin Madison

Earlence Fernandes

University of Wisconsin Madison

Proceedings - IEEE Symposium on Security and Privacy

10816011 (ISSN)

Vol. 2021-May 501-518

42nd IEEE Symposium on Security and Privacy, SP 2021
Virtual, San Francisco, USA,



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