Obfuscation and Strict Online Anonymity

I consider the case for genuinely anonymous web searching. Big data seems to have it in for privacy. The story is well known, particularly since the dawn of the web. Vastly more personal information, monumental and quotidian, is gathered than in the pre-digital days. Once gathered it can be aggregated and analyzed to produce rich portraits, which in turn permit unnerving prediction of our future behavior. The new information can then be shared widely, limiting prospects and threatening autonomy. How should we respond? Following Nissenbaum (2011) and Brunton and Nissenbaum (2011 and 2013), I will argue that the proposed solutions—consent, anonymity as conventionally practiced, corporate best practices, and law—fail to protect us against routine surveillance of our online behavior. Brunton and Nissenbaum rightly maintain that, given the power imbalance between data holders and data subjects, obfuscation of one’s online activities is justified. Obfuscation works by generating “misleading, false, or ambiguous data with the intention of confusing an adversary or simply adding to the time or cost of separating good data from bad,” thus decreasing the value of the data collected (Brunton and Nissenbaum, 2011). The phenomenon is as old as the hills. Natural selection evidently blundered upon the tactic long ago. Take a savory butterfly whose markings mimic those of a toxic cousin. From the point of view of a would-be predator the data conveyed by the pattern is ambiguous. Is the bug lunch or potential last meal? In the light of the steep costs of a mistake, the savvy predator goes hungry. Online obfuscation works similarly, attempting for instance to disguise the surfer’s identity (Tor) or the nature of her queries (Howe and Nissenbaum 2009). Yet online obfuscation comes with significant social costs. First, it implies free riding. If I’ve installed an effective obfuscating program, I’m enjoying the benefits of an apparently free internet without paying the costs of surveillance, which are shifted entirely onto non-obfuscators. Second, it permits sketchy actors, from child pornographers to fraudsters, to operate with near impunity. Third, online merchants could plausibly claim that, when we shop online, surveillance is the price we pay for convenience. If we don’t like it, we should take our business to the local brick-and-mortar and pay with cash. Brunton and Nissenbaum have not fully addressed the last two costs. Nevertheless, I think the strict defender of online anonymity can meet these objections. Regarding the third, the future doesn’t bode well for offline shopping. Consider music and books. Intrepid shoppers can still find most of what they want in a book or record store. Soon, though, this will probably not be the case. And then there are those who, for perfectly good reasons, are sensitive about doing some of their shopping in person, perhaps because of their weight or sexual tastes. I argue that consumers should not have to pay the price of surveillance every time they want to buy that catchy new hit, that New York Times bestseller, or a sex toy.

Protecting Your Search Privacy: A Lesson Plan

Each year search engines like Google, Bing and Yahoo, complete trillions of search queries online. Students are especially dependent on these search tools because of their popularity, convenience and accessibility. However, what students are unaware of, by choice or naiveté is the amount of personal information that is collected during each search session, how that data is used and who is interested in their online behavior profile. Privacy policies are frequently updated in favor of the search companies but are lengthy and often are perused briefly or ignored entirely with little thought about how personal web habits are being exploited for analytics and marketing. As an Information Literacy instructor, and a member of the Electronic Frontier Foundation, I believe in the importance of educating college students and web users in general that they have a right to privacy online. Class discussions on the topic of web privacy have yielded an interesting perspective on internet search usage. Students are unaware of how their online behavior is recorded and have consistently expressed their hesitancy to use tools that disguise or delete their IP address because of the stigma that it may imply they have something to hide or are engaging in illegal activity. Additionally, students fear they will have to surrender the convenience of uber connectivity in their applications to maintain their privacy. The purpose of this lightning presentation is to provide educators with a lesson plan highlighting and simplifying the privacy terms for the three major search engines, Google, Bing and Yahoo. This presentation focuses on what data these search engines collect about users, how that data is used and alternative search solutions, like DuckDuckGo, for increased privacy. Students will directly benefit from this lesson because informed internet users can protect their data, feel safer online and become more effective web searchers.

Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT.