As most readers are likely aware, the Federal Communications Commission (FCC) issued a rule last fall governing how Internet service providers (ISPs) can gather and share data about consumers that was recently rolled back through the Congressional Review Act. The media stoked consumer fear with headlines such as “For Sale: Your Private Browsing History” and comments about how ISPs can now “sell your Web browsing history to advertisers“. We also saw promises from large ISPs such as Comcast promising not to do exactly that. What’s next is anyone’s guess, but technologists need not stand idly by.
Technologists can and should play an important role in this discussion in several ways. In particular, conveying knowledge about the capabilities and uses of network monitoring, and developing both new monitoring technologies and privacy-preserving capabilities can and should shape this debate in three important ways: (1) Level-setting on the data collection capabilities of various parties; (2) Understanding and limiting the power of inference; and (3) Developing new monitoring technologies that help facilitate network operations and security while protecting consumer privacy.
1. Level-setting on data collection uses and capabilities. Before entering a debate about privacy, it helps to have a firm understanding of who can collect what types of data—both in theory and in practice, as well as the myriad ways that data might be used for good (and bad). For example, in practice, if anyone has your browsing history, your ISP is a less likely culprit than an online service provider such as Google—who operates a browser, and (perhaps more importantly) whose analytics scripts are on a large fraction of the Internet’s web pages. Your browsing is also likely being logged by many of the countless online trackers that keep track of your browsing history, often without your knowledge or consent. In contrast, the network monitoring technology that is available in routers and switches today makes it a lot more difficult to extract “browsing history”; that requires a technology commonly referred to as “deep packet inspection” (DPI), or complete capture of network traffic data, which is expensive to deploy, and even more costly when data storage and analysis is concerned. Most ISPs will tell you than DPI is deployed on only a small fraction of the links in their networks, and that fraction is going down as speeds are increasing; it’s expensive to collect and analyze all of that data.
ISPs do, of course, collect other types of traffic statistics, such as lookups to domain names via the Domain Name System (DNS) and coarse-grained traffic volume statistics via IPFIX. That data can, of course, be revealing. At the same time, ISPs will correctly point out that monitoring DNS and IPFIX is critical to securing and operating the network. DNS traffic, for example, is central to detecting denial of service attacks or infected devices. IPFIX statistics are critical for monitoring and mitigating network congestion. DNS is a quintessential example of data that is both incredibly sensitive (because it reveals the domains and websites we visit, among other things, and is typically unencrypted) and incredibly useful for detecting attacks, ranging from phishing to denial of service attacks.
The long line of security and traffic engineering research illustrates both the importance of data collection, as well as the limitations of current network monitoring capabilities in performing these tasks. Take, for example, research on botnet detection, which has shown the power of using DNS lookup data and IPFIX statistics for detecting compromise and intrusion. Or, the development of traffic engineering capabilities in the data center and in the wide area, which depend on the collection and analysis of IPFIX records and in some cases packet traces.
2. Understanding (and mitigating) the power of inference. While most of the focus in the privacy debate thus far concerns data collection (specifically, a focus on DPI, which is somewhat misguided per the discussion above), we would be wise to also consider what can be inferred from any data that is collected. For example, various aspects of “browsing history” could be evident from various datasets ranging from DNS to DPI, but as discussed above all of these datasets also have legitimate operational uses. Furthermore, “browsing history” is evident from a wide range of datasets that many parties are privy to without our consent, beyond just ISPs. Such inference capabilities are only going to increase with the proliferation of data-producing Internet-connected devices coupled with advances in machine learning. If prescriptive rules specify which some types of data can be collected, we risk over-prescribing rules, while failing to achieve the goal of protecting the higher-level information that we really want to protect.
While asking questions about collection is a fine place to start a discussion, we should be at least as concerned with how the data is used, what it can be used to infer, and who it is shared with.We likely should be asking: (1) What data do we think should be protected or private? (2) What types network data permits inference of that private data? (3) Who has access to that data and under what circumstances? Suppose that I am interested in protecting information about whether I am at home. My ISP could learn this information from my traffic patterns, simply based on the decline in traffic volume from individual devices, even if all of my web traffic were encrypted, and even if I used a virtual private network (VPN) for all of my traffic. Such inference will be increasingly possible as more devices in our homes connect to the Internet. But, online service providers could also come to know the same information without my consent, based on different data; Google, for example, would know that I’m browsing the web at my office, rather than at home, through the use of technologies such as cookies, browser fingerprinting, and other online device tracking mechanisms.
Past and ongoing research, such as the Web Transparency and Accountability Project, as well as the “What They Know” series from the Wall Street Journal, shed important light on what can be inferred from various digital data sources. The Upturn report last year was similarly illuminating with respect to ISP data. More recently, researchers at Princeton including Noah Apthorpe and Dillon Reisman have been developing techniques to mitigate the power of inference using various traffic shaping and camouflaging techniques to limit what an ISP can infer from traffic patterns coming from a home network.
3. Facilitating purpose-driven network measurement and data minimization. Part of the tension surrounding network measurement and privacy is that current network monitoring technology is very crude; in fact, this technology hasn’t changed considerably in nearly 30 years. It at once gathers too much data, and yet, for many purposes, it is still too little. Consider, for example, that with current network monitoring technology, an ISP (or content provider) have incredible difficulty determining a user’s quality of experience for a given application, such as video streaming, simply because the wrong kind of data is collected, at the wrong granularity. As a result, ISPs (and many other parties in the Internet ecosystem) adopt a post hoc “collect first, ask questions later” approach, simply because current network monitoring technology (1) is oriented towards offline processing on warehoused data; (2) does not make it easy to figure out what data is needed to answer a particular analysis question.
Instead, network data collection could be driven by the questions operators were asking; data could be collected if—and only if—it were pertinent to a specific question or network operations task, such as monitoring application performance or detecting attacks. For example, suppose that an operator could ask a query such as “tell me the average packet loss rate of all Netflix video streams for subscribers in Seattle”. Answering such a query with today’s tools is challenging: one would have to collect all packet traces and all DNS queries and somehow identify post hoc that these streams correspond to the application of interest. In short, it’s difficult, if not impossible, answer such an operational query today without large-scale collection and storage of (very sensitive) data—all to find what is essentially a needle in a haystack.
Over the past year, my Ph.D. student Arpit Gupta at Princeton has been leading the design and development of a system called Sonata that may ultimately resolve this dichotomy and give us the best of both worlds. Two emerging technologies—(1) in-band network measurement, as supported by Barefoot’s Tofino chipset; (2) scalable streaming analytics platforms such as Spark—make it possible to write a high-level query in advance and only collect the data that is needed to satisfy the query. Such technology allows a network operator to write a query in a high-level language (in this case, Scala), specifying only the question, but allowing the runtime to figure out the minimal set of raw data that is needed to satisfy the operator’s query.
Our goal in the design and implementation of Sonata was to satisfy the operational and scaling limitations of network measurement, but achieving such scalability also has data minimization effects that have positive benefits for privacy. Data that is collected can also be a liability; it may, for example, become the target of law enforcement requests or subpoenas, which parties such as ISPs, but also online providers such as Google are regularly subject to. Minimizing the collected data to only that which is pertinent to operational queries can also ultimately help reduce this risk.
Sonata is open source, and we welcome contributions and suggestions from the community about how we can better support specific types of network queries and tasks.
Summary. Network monitoring and analytics technology is moving at a rapid pace, in terms of its capabilities to help network operators answer important questions about performance and security, without coming at the cost of consumer privacy. Technologists should devote attention to developing new technologies that can help achieve the best of both worlds, and on helping educate policymakers about the capabilities (and limitations) of existing network monitoring technology. Policymakers should be aware that network monitoring technology continues to advance, and should focus discussion around protecting what can be inferred, rather than focusing only on who can collect a packet trace.
