As social recommendations such as friend suggestions and people to follow become increasingly popular and influential on the growth of social media, we find that prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy. To study this imbalance in online equal opportunities, we leverage new Instagram data and offer for the first time an analysis that studies the effect of gender, homophily and growth dynamics under social recommendations. Our mathematical analysis demonstrates the existence of an algorithmic glass ceiling that exhibits all the properties of the metaphorical social barrier that hinders groups like women or people of color from attaining equal representation. What raises concern is that our proof shows that under fixed minority and homophily parameters the algorithmic effect is systematically larger than the glass ceiling generated by the spontaneous growth of social networks. We discuss ways to address this concern in future design.

Linking accounts of the same user across datasets -- even when personally identifying information is removed or unavailable -- is an important open problem studied in many contexts. Beyond many practical applications, (such as cross domain analysis, recommendation, and link prediction), understanding this problem more generally informs us on the privacy implications of data disclosure. Previous work has typically addressed this question using either different portions of the same dataset or observing the same behavior across thematically similar domains. In contrast, the general cross-domain case where users have different profiles independently generated from a common but unknown pattern raises new challenges, including difficulties in validation, and remains under-explored.

Generations of computer programmers are taught to prefetch network objects in computer science classes. In practice, prefetching can be harmful to the user's wallet when she is on a limited or pay-per-byte cellular data plan. Many popular, professionally-written smartphone apps today prefetch large amounts of network data that the typical user may never use. We present Procrastinator, which automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on the user's data plan, and whether the object is needed at the present time. Procrastinator does not require app developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app. These savings for the data-poor user come with a 300ms median latency penalty on LTE.

Many popular, professionally-written smartphone apps today prefetch large amounts of network data to improve performance. However, the typical user may not use all of this network data. When a user is on a limited or pay-per-byte cellular data plan, such as when roaming internationally, this prefetching behavior can cost her in overage fees on her cellular bill. This video demonstrates Procrastinator, which is a system that automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on her data plan, and whether the object is needed at the present time. Procrastinator does not require app developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app. These savings for the data-poor user come with a 300ms median latency penalty on LTE if the user goes to a part of the app where Procrastinator did not allow data to be prefetched. This video shows how main content on the primary page of apps is unaffected, and the delay that the user will typically experience if she goes to secondary pages in apps when she is running out of cellular data plan bytes.

Generations of computer programmers are taught to prefetch network objects in computer science classes. In practice, prefetching can be harmful to the user's wallet when she is on a limited or pay-per-byte cellular data plan. Many popular, professionally-written smartphone apps today prefetch large amounts of network data that the typical user may never use. We present Procrastinator, which automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on the user's data plan, and whether the object is needed at the present time. Procrastinator does not require developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X savings in bytes transferred, depending on the user and the app. In theory, we can achieve 17X savings, but we need to overcome additional technical challenges.

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.

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.