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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(More)
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(More)
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 <i>path explosion</i> phenomenon between most pairs of nodes.(More)
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(More)
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(More)
—The increase in data consumed by smart-phones 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(More)
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(More)
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(More)
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(More)
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(More)