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.

Distributing long-tail content is a difficult task due to the low amortization of bandwidth transfer costs as such content has limited number of views. Two recent trends are making this problem harder. First, the increasing popularity of user-generated content and online social networks create and reinforce such popularity distributions. Second, the recent trend of geo-replicating content across multiple points of presence spread around the world, done for improving quality of experience (QoE) for users. In this paper, we analyze and explore the tradeoff involving the “freshness” of the information available to the users and WAN bandwidth costs, and we propose ways to reduce the latter through smart update propagation scheduling, by leveraging on the knowledge of the mapping between social relationships and geographic location, the timing regularities and time differences in end user activity. We first assess the potential of our approach by implementing a simple social-aware scheduling algorithm that operates under bandwidth budget constraints and by quantifying its benefits through a trace-driven analysis. We show that it can reduce WAN traffic by up to 55 percent compared to an immediate update of all replicas, with a minimal effect on information freshness and latency. Second, we build TailGate, a practical system that implements our social-aware scheduling approach, which distributes on the fly long-tail content across PoPs at reduced bandwidth costs by flattening the traffic. We evaluate TailGate by using traces from an OSN and show that it can decrease WAN bandwidth costs by as much as 80 percent and improve QoE. We deploy TailGate on PlanetLab and show that even in the case when imprecise social information is available, it can still decrease by a factor of 2 the latency for accessing long-tail YouTube videos.

Mobile phones in the 3G/4G era enable us to stay connected not only to the voice network, but also to online services like social networks. In this paper, we study the energy and network costs of mobile applications that provide continuous online presence (e.g. WhatsApp, Facebook, Skype). By combining measurements taken on the mobile and the cellular access network, we reveal a detailed picture of the mechanisms selected to implement online presence, along with their effect on handset energy consumption and network signaling traffic.

Content caching is a fundamental building block of the Internet. Caches are widely deployed at network edges to improve performance for end-users, and to reduce load on web servers and the backbone network. Considering mobile 3G/4G networks, however, the bottleneck is at the access link, where bandwidth is shared among all mobile terminals. As such, per-user capacity cannot grow to cope with the traffic demand. Unfortunately, caching policies would not reduce the load on the wireless link which would have to carry multiple copies of the same object that is being downloaded by multiple mobile terminals sharing the same access link.

After years of speculation, price discrimination in e-commerce 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., x2. To the best of our efforts we could not attribute the observed price gaps to currency, shipping, or taxation differences.

The co-existence of cellular and wired networks has been exploited almost exclusively in the direction of OffLoading traffic from the former onto the latter. In this paper we claim that there exist cases that call for the exact opposite, i.e, use the cellular network to assist a fixed wired network. In particular, we show that by "OnLoading'' traffic from the wired broadband network onto the cellular network we can usefully speedup wired connections, on the downlink or the uplink. We consider the technological challenges pertaining to this idea and implement a prototype 3G OnLoading service that we call 3GOL, that can be deployed by an operator providing both the wired and cellular network services. By strategically OnLoading a fraction of the data transfers to the 3G network, one can significantly enhance the performance of particular applications. In particular we demonstrate non-trivial performance benefits of 3GOL to two widely used applications: video-on-demand and multimedia upload. We also consider the case when the operator that provides wired and cellular services is different, adding the analysis on economic constraints and volume cap on cellular data plans that need to be respected. Simulating 3GOL}over a DSLAM trace we show that 3GOL can reduce video pre-buffering time by at least 20% for 50% of the users while respecting data caps and we design a simple estimator to compute the daily allowance that can be used towards 3GOL while respecting caps. Our prototype is currently being piloted in 30 households in a large European city by a large network provider.

The large-scale collection and exploitation of personal information to drive targeted online advertisements has raised privacy concerns. As a step towards understanding these concerns, we study the relationship between how much information is collected and how valuable it is for advertising. We use HTTP traces consisting of millions of users to aid our study and also present the first comparative study between aggregators. We develop a simple model that captures the various parameters of today's advertising revenues, whose values are estimated via the traces. Our results show that per aggregator revenue is skewed (5% accounting for 90% of revenues), while the contribution of users to advertising revenue is much less skewed (20% accounting for 80% of revenue). Google is dominant in terms of revenue and reach (presence on 80% of publishers). We also show that if all 5% of the top users in terms of revenue were to install privacy protection, with no corresponding reaction from the publishers, then the revenue can drop by 30%.

Voice and data traffic growth over the last several years has become a major challenge for cellular operators with a direct impact on revenues, infrastructure investments, and end-user performance. The economics of these operators depend on various incentives used to attract users in the form of unlimited, buffet-like voice/sms/data packages. However, our understanding of the effects of user behavior under these offerings on operator revenues/costs remains poor. Using two years of detailed usage information of ~1 million users across three services, voice, sms and data, combined with payment and cost information, we study how user behavior affects the economics of cellular operators. We discover that around 20% of the users consume more resources than what they pay for and hence are non-profitable. In addition to the individual user behavior, we study how the user interactions in the call graph affect the operator's revenues and cost, drawing on tools from social network analysis. We develop a framework that incorporates both the individual and social user behavior for studying how volume caps influence the revenues and the traffic costs. Using this framework we empirically show that volume caps can increase the difference between the revenues and the traffic costs of the studied operator by a factor of 2, while affecting only 16% of the existing user base.

Most online service providers offer free services to users and in part, these services collect and monetize personally identifiable information (PII), primarily via targeted advertisements. Against this backdrop of economic exploitation of PII, it is vital to understand the value that users put to their own PII. Although studies have tried to discover how users value their privacy, little is known about how users value their PII while browsing, or the exploitation of their PII. Extracting valuations of PII from users is non-trivial - surveys cannot be relied on as they do not gather information of the context where PII is being released, thus reducing validity of answers. In this work, we rely on refined Experience Sampling - a data collection method that probes users to valuate their PII at the time and place where it was generated in order to minimize retrospective recall and hence increase measurement validity. For obtaining an honest valuation of PII, we use a reverse second price auction. We developed a web browser plugin and had 168 users - living in Spain - install and use this plugin for 2 weeks in order to extract valuations of PII in different contexts.

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.

The assumption of rationality is fundamental to large part of network economics literature. In this paper, we use a simple definition of rationality based on economic self-interest and test for such behavior using real data on how users purchase and consume mobile network services. If users acted in their best (optimal) interest, then they would opt for the tariff that best suits their demands. However, that need not be the case, as users can fall prey to biases that can lead them to make seemingly sub-optimal choices. Such biases are hard to characterize and in this paper we empirically study how end-users purchase and use network services.

Access link can often be the bottleneck for application performance. In this paper, we propose to augment wired connections using cellular ones, that we term "3G onloading (3GOL)". 3GOL utilizes available mobile devices and already-paid-for data volumes to augment and improve performance of applications on wired network. We motivate 3GOL by understanding bottlenecks present in the wired and the cellular networks. In order to understand the potential benefits of 3GOL, we conduct active experiments using mobile devices. We show that capacity gains can scale linearly with the number of devices on the downlink while also seeing improvements on the uplink. Using real traces we show how video on demand can benefit with 3GOL, even when volume caps are in place. We design 3GOL as an over the top service, and highlight research challenges.

Distributing long-tail content is an inherently difficult task due to the low amortization of bandwidth transfer costs as such content has limited number of views. Two recent trends are making this problem harder. First, the increasing popularity of user-generated content (UGC) and online social networks (OSNs) create and reinforce such popularity distributions. Second, the recent trend of geo-replicating content across multiple PoPs spread around the world, done for improving quality of experience (QoE) for users and for redundancy reasons, can lead to unnecessary bandwidth costs. We build TailGate, a system that exploits social relationships, regularities in read access patterns, and time-zone differences to efficiently and selectively distribute long-tail content across PoPs. We evaluate TailGate using large traces from an OSN and show that it can decrease WAN bandwidth costs by as much as 80% as well as reduce latency, improving QoE. We deploy TailGate on PlanetLab and show that even in the case when imprecise social information is available, TailGate can still decrease the latency for accessing long-tail YouTube videos by a factor of 2.

The difficulty of partitioning social graphs has introduced new system design challenges for scaling of online social networks (OSNs). Vertical scaling by resorting to full replication can be a costly proposition. Scaling horizontally by partitioning and distributing data among multiple servers using, for e.g., distributed hash tables (DHTs), can suffer from expensive interserver communication. Such challenges have often caused costly rearchitecting efforts for popular OSNs like Twitter and Facebook. We design, implement, and evaluate SPAR, a Social Partitioning and Replication middleware that mediates transparently between the application and the database layer of an OSN. SPAR leverages the underlying social graph structure in order to minimize the required replication overhead for ensuring that users have their neighbors' data colocated in the same machine. The gains from this are multifold: Application developers can assume local semantics, i.e., develop as they would for a single machine; scalability is achieved by adding commodity machines with low memory and network I/O requirements; and N+K redundancy is achieved at a fraction of the cost. We provide a complete system design, extensive evaluation based on datasets from Twitter, Orkut, and Facebook, and a working implementation. We show that SPAR incurs minimum overhead, can help a well-known Twitter clone reach Twitter's scale without changing a line of its application logic, and achieves higher throughput than Cassandra, a popular key-value store database.

Monetizing personal information is a key economic driver of online industry. End-users are becoming more concerned about their privacy, as evidenced by increased media attention. This paper proposes a mechanism called 'transactional' privacy that can be applied to personal information of users. Users decide what personal information about themselves is released and put on sale while receiving compensation for it. Aggregators purchase access to exploit this information when serving ads to a user. Truthfulness and efficiency, attained through an unlimited supply auction, ensure that the interests of all parties in this transaction are aligned. We demonstrate the effectiveness of transactional privacy for web-browsing using a large mobile trace from a major European capital. We integrate transactional privacy in a privacy-preserving system that curbs leakage of information. These mechanisms combine to form a market of personal information that can be managed by a trusted third party.

The increase in data consumed by smartphones is becoming a huge problem for mobile operators. In three years, mobile data traffic in AT&T's network rose 5000%. The US operators invest $50 billion in the data networks every year and the technology upgrades and innovation still fail to keep up with the demand. In this paper we design two algorithms for delay-tolerant offloading of bulky, socially recommended content from 3G networks. The first one, called "MixZones", uses opportunistic, ad hoc transfers between users, and is assisted by predictions made by the network operator. The second one, called "HotZones", exploits delay tolerance and tries to download contents when users are close to Wi-Fi access points; it is also assisted by predictions made by the operator. We evaluate both algorithms using a large data set, obtained from a major mobile operator and a realistic application similar to Apple's Ping music social network. The metrics address the amount of offloading, delay and mobile energy efficiency. We find that both solutions succeed in offloading a significant amount of traffic, with a positive impact on user battery lifetime. Surprisingly, we also find that all the benefit obtained from the operator with the MixZones algorithm (i.e with ad hoc exchanges between users) can be achieved with the HotZones algorithm and a small investment in Wi-Fi access points. Note that the latter is considerably less complex to deploy than the former.

In recent years there has been renewed interest in the problem of transferring bulk data (terabytes) utilizing commercial ISPs. The need to transfer bulk data arises in various scientific and industrial applications. Today, this data is moved using postal service in conjunction with hard drives and DVDs or special high performance dedicated networks. The key insight underlying the recent work was that many of the applications are delay- tolerant and hence the bulk data can be transferred at minimal cost, utilizing already paid-for off-peak bandwidth resulting from diurnal traffic patterns, using store-and-forward through intermediate storage nodes. In this paper we expand on this theme and consider the computational complexity of transferring data over a network whose links have time-varying capacities. We show that the general problem of finding a cost-optimal transfer of the bulk data can be solved in polynomial-time using minimum cost flow algorithms on a time-expanded version of the underlying network. Our solution involves graph transformations. We present additional transformations that enable the handling of half-duplex links (e.g. fiber-optic links) as well as node processing constraints (e.g. limitations on the processing power available for filtering or archiving). An important characteristic of our solution is the ability to handle nodes with storage. We consider nodes with storage that varies over time in terms of both capacity and cost. We show that our approach provably extends to cover the case of linear costs, providing polynomial- time algorithms. However, the flat-fee storage model is NP- complete and hence unlikely to be tractable in polynomial-time. Interestingly, with constrained storage, the optimal solutions may involve loops, i.e. the data may pass through the same node more than once on its way from the destination to the source along the optimal route. We use data from one of the world's leading ISPs and perform a comprehensive evaluation of our algorithm. We show that there exists a huge potential for cost savings in real-world networks with time-varying costs for both link capacities and node storage.

The difficulty of scaling Online Social Networks (OSNs) has introduced new system design challenges that has often caused costly re-architecting for services like Twitter and Facebook. The complexity of interconnection of users in social networks has introduced new scalability challenges. Conventional vertical scaling by resorting to full replication can be a costly proposition. Horizontal scaling by partitioning and distributing data among multiples servers - e.g. using DHTs - can lead to costly inter-server communication.

Mobile opportunistic networks are characterized by unpredictable mobility, heterogeneity of contact rates and lack of global information. Successful delivery of messages at low costs and delays in such networks is thus challenging. Most forwarding algorithms avoid the cost associated with flooding the network by forwarding only to nodes that are likely to be good relays, using a quality metric associated with nodes. However it is non-trivial to decide whether an encountered node is a good relay at the moment of encounter. Thus the problem is in part one of online inference of the quality distribution of nodes from sequential samples, and has connections to optimal stopping theory. Based on these observations we develop a new strategy for forwarding, which we refer to as delegation forwarding.

Forwarding in Delay Tolerant Networks (DTNs) is a challenging problem. We focus on the specific issue of forwarding in an environment where mobile devices are carried by people in a restricted physical space (a conference) and contact patterns are not predictable. We show for the first time a path explosion phenomenon between most pairs of nodes. This means that, once the first path reaches the destination, the number of subsequent paths grows rapidly with time, so there usually exist many near-optimal paths. We study the path explosion phenomenon both analytically and empirically. Our results highlight the importance of unequal contact rates across nodes for understanding the performance of forwarding algorithms. We also find that a variety of well-known forwarding algorithms show surprisingly similar performance in our setting and we interpret this fact in light of the path explosion phenomenon.

A common assumption made in traffic matrix (TM) modeling and estimation is independence of a packet's network ingress and egress. We argue that in real IP networks, this assumption should not and does not hold. The fact that most traffic consists of two-way exchanges of packets means that traffic streams flowing in opposite directions at any point in the network are not independent. In this paper we propose a model for traffic matrices based on independence of connections rather than packets. We argue that the independent-connection (IC) model is more intuitive, and has a more direct connection to underlying network phenomena than the gravity model. To validate the IC model, we show that it fits real data better than the gravity model and that it works well as a prior in the TM estimation problem. We study the model's parameters empirically and identify useful stability properties. This justifies the use of the simpler versions of the model for TM applications. To illustrate the utility of the model we focus on two such applications: synthetic TM generation and TM estimation. To the best of our knowledge this is the first traffic matrix model that incorporates properties of bidirectional traffic.

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.