Price discrimination, setting the price of a given product for each customer individually according to his valuation for it, can benefit from extensive information collected online on the customers and thus contribute to the profitability of e-commerce services. Another way to discriminate among customers with different willingness to pay is to steer them towards different sets of products when they search within a product category (i.e., search discrimination). Our main contribution in this paper is to empirically demonstrate the existence of signs of both price and search discrimination on the Internet, and to uncover the information vectors used to facilitate them. Supported by our findings, we outline the design of a large-scale, distributed watchdog system that allows users to detect discriminatory practices.

After years of speculation, price discrimination in ecommerce driven by the personal information that users leave (involuntarily) online, has started attracting the attention of privacy researchers, regulators, and the press. In our previous work we demonstrated instances of products whose prices varied online depending on the location and the characteristics of prospective online buyers. In an effort to scale up our study we have turned to crowd-sourcing. Using a browser extension we have collected the prices obtained by an initial set of 340 test users as they surf the web for products of their interest. This initial dataset has permitted us to identify a set of online stores where price variation is more pronounced. We have focused on this subset, and performed a systematic crawl of their products and logged the prices obtained from different vantage points and browser configurations. By analyzing this dataset we see that there exist several retailers that return prices for the same product that vary by 10%-30% whereas there also exist isolated cases that may vary up to a multiplicative factor, e.g., ×2. To the best of our efforts we could not attribute the observed price gaps to currency, shipping, or taxation differences.

Online Behavioural targeted Advertising (OBA) has risen in prominence as a method to increase the effectiveness of online advertising. OBA operates by associating tags or labels to users based on their online activity and then using these labels to target them. This rise has been accompanied by privacy concerns from researchers, regulators and the press. In this paper, we present a novel methodology for measuring and understanding OBA in the online advertising market. We rely on training artificial online personas representing behavioural traits like 'cooking', 'movies', 'motor sports', etc. and build a measurement system that is automated, scalable and supports testing of multiple configurations. We observe that OBA is a frequent practice and notice that categories valued more by advertisers are more intensely targeted. In addition, we provide evidences showing that the advertising market targets sensitive topics (e.g, religion or health) despite the existence of regulation that bans such practices. We also compare the volume of OBA advertising for our personas in two different geographical locations (US and Spain) and see little geographic bias in terms of intensity of OBA targeting. Finally, we check for targeting with do-not-track (DNT) enabled and discover that DNT is not yet enforced in the web.

We present the design and evaluation of ITMgen, a tool for generating synthetic but representative Interdomain Traffic Matrices (ITMs). ITMgen is motivated by the observation that gravity-based models do not reflect application level or regional characteristics of Internet traffic. ITMgen works at the level of connections, taking into account the relative sizes of ASes, their popularity with respect to various applications, and the relation between forward and reverse traffic for different application types. The necessary parameters for integrating application types and the distribution of content popularity can be realistically estimated by combining public sources like Alexa that capture traffic trends at a macro level with local traffic sampling (NetFlow, DPI) for providing an additional enhancement layer at the micro level. Using the above philosophy we demonstrate that we can synthesize ITMs that match realworld measurements closer than the current state of the art. In addition, the modular design philosophy of ITMgen makes it easy to integrate additional enhancement layers that improve the accuracy of our existing implementation.

Abstract. Identifying the statistical properties of the Interdomain Traffic Matrix (ITM) is fundamental for Internet techno-economic studies but challenging due to the lack of adequate traffic data. In this work, we utilize a Europe-wide measurement infrastructure deployed at the GÉANT backbone network to examine some important spatial properties of the ITM. In particular, we analyze its sparsity and characterize the distribution of traffic generated by different ASes. Our study reveals that the ITM is sparse and that the traffic sent by an AS can be modeled as the LogNormal or Pareto distribution, depending on whether the corresponding traffic experiences congestion or not. Finally, we show that there exist significant correlations between different ASes mostly due to relatively few highly popular prefixes.