Andrei Sabelfeld

Yunang Chen, Amrita Roy Chowdhury, Ruizhe Wang et al.

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%.

Cristian-Alexandru Staicu, Daniel Schoepe, Musard Balliu et al.

Information flow analysis prevents secret or untrusted data from flowing into public or trusted sinks. Existing mechanisms cover a wide array of options, ranging from lightweight taint analysis to heavyweight information flow control that also considers implicit flows. Dynamic analysis, which is particularly popular for languages such as JavaScript, faces the question whether to invest in analyzing flows caused by not executing a particular branch, so-called hidden implicit flows. This paper addresses the questions how common different kinds of flows are in real-world programs, how important these flows are to enforce security policies, and how costly it is to consider these flows. We address these questions in an empirical study that analyzes 56 real-world JavaScript programs that suffer from various security problems, such as code injection vulnerabilities, denial of service vulnerabilities, memory leaks, and privacy leaks. The study is based on a state-of-the-art dynamic information flow analysis and a formalization of its core. We find that implicit flows are expensive to track in terms of permissiveness, label creep, and runtime overhead. We find a lightweight taint analysis to be sufficient for most of the studied security problems, while for some privacy-related code, observable tracking is sometimes required. In contrast, we do not find any evidence that tracking hidden implicit flows reveals otherwise missed security problems. Our results help security analysts and analysis designers to understand the cost-benefit tradeoffs of information flow analysis and provide empirical evidence that analyzing implicit flows in a cost-effective way is a relevant problem.