We study the impact of consumer quality-based self-selection on online reviews. Consumers differ in their expertise, which has two effects. First, expertise is instrumental to choice: Experts purchase better products than Non-Experts. Second, because of their superior choices, Experts endogenously form higher reference points, which leads them to post harsher ratings for a given quality. Combined, these two facts imply a bias against higher-quality products. When this bias gets large, ratings are non-monotonic in quality: lower-quality products can obtain higher ratings than superior alternatives, thanks to the lower standard they are held to.

Many mobile services consist of two components: a server providing an API, and an application running on smartphones and communicating with the API. An unresolved problem in this design is that it is difficult for the server to authenticate which app is accessing the API. This causes many security problems. For example, the provider of a private network API has to embed secrets in its official app to ensure that only this app can access the API; however, attackers can uncover the secret by reverse-engineering. As another example, malicious apps may send automatic requests to ad servers to commit ad fraud.

Transport Layer Security (TLS) is the most commonly used security protocol to encrypt web traffic. TLS connections are computationally expensive to set up, so the TLS protocol supports session resumption, where previously negotiated connection parameters can be used to short- circuit the TLS handshake. The server assigns new sessions a session identifier (ID) and caches each session by its ID so it can be retrieved later. As clients come and go, sessions in the server's cache will have to be evicted according to the server's eviction policy. We find that first-in-first-out (FIFO) and least-recently- used (LRU) are the most common session cache eviction policies among popular TLS libraries, however, for applications whose clients connect at regular intervals, such as mobile advertising, the performance of these policies may be far worse than randomly evicting policies from the cache. To handle this, we propose a novel eviction policy for TLS session caches, predictive eviction, that relies on the server knowing the next time each client will connect again. Using a real-world application of such a policy, Android in-application advertising, we build a client that is able to simulate the behavior of a large number of devices requesting mobile advertisements over TLS. We use this simulated client to benchmark the hit rate of the predictive policy compared with eviction policies found in popular TLS library implementations. In addition, we demonstrate that our policy can be implemented efficiently by benchmarking its performance in transactions per second compared with OpenSSL's session cache implementation, and compared with TLS session tickets (an alternative to session caching for resuming TLS sessions). We find that our policy has better hit rate performance than other eviction policies, and can achieve comparable performance to session tickets. To the best of our knowledge, this is the first study of the performance of TLS session resumption strategies.

Many Android applications are distributed for free but are supported by advertisements. Ad libraries embedded in the app fetch content from the ad provider and display it on the app's user interface. The ad provider pays the developer for the ads displayed to the user and ads clicked by the user. A major threat to this ecosystem is ad fraud, where a miscreant's code fetches ads without displaying them to the user or "clicks" on ads automatically. Ad fraud has been extensively studied in the context of web advertising but has gone largely unstudied in the context of mobile advertising.

Malicious activities involving Android applications are rising rapidly. As prior work on cyber-crimes suggests, we need to understand the economic incentives of the criminals to design the most effective defenses. In this paper, we investigate application plagiarism on Android markets at a large scale. We take the first step to characterize plagiarized applications and estimate their impact on the original application developers. We first crawled 265,359 free applications from 17 Android markets around the world and ran a tool to identify similar applications ("clones"). Based on the data, we examined properties of the cloned applications, including their distribution across different markets, application categories, and ad libraries. Next, we examined how cloned applications affect the original developers. We captured HTTP advertising traffic generated by mobile applications at a tier-1 US cellular carrier for 12 days. To associate each Android application with its advertising traffic, we extracted a unique advertising identifier (called the client ID) from both the applications and the network traces. We estimate a lower bound on the advertising revenue that cloned applications siphon from the original developers, and the user base that cloned applications divert from the original applications. To the best of our knowledge, this is the first large scale study on the characteristics of cloned mobile applications and their impact on the original developers.

Malicious activities involving Android applications are rising rapidly. As prior work on cyber-crimes suggests, we need to understand the economic incentives of the criminals to design the most effective defenses. In this paper, we investigate application plagiarism on Android markets at a large scale. We take the first step to characterize plagiarized applications and estimate their impact on the original application developers. We first crawled 265,359 free applications from 17 Android markets around the world and ran a tool to identify similar applications ("clones"). Based on the data, we examined properties of the cloned applications, including their distribution across different markets, application categories, and ad libraries. Next, we examined how cloned applications affect the original developers. We captured HTTP advertising traffic generated by mobile applications at a tier-1 US cellular carrier for 12 days. To associate each Android application with its advertising traffic, we extracted a unique advertising identifier (called the client ID) from both the applications and the network traces. We estimate a lower bound on the advertising revenue that cloned applications siphon from the original developers, and the user base that cloned applications divert from the original applications. To the best of our knowledge, this is the first large scale study on the characteristics of cloned mobile applications and their impact on the original developers.

Security policies, which specify what applications are allowed to do, are notoriously difficult to specify correctly. Many applications were found to request over-liberal permissions. On mobile platforms, this might prevent a cautious user from installing an otherwise harmless application or, even worse, increase the attack surface in vulnerable applications. As a result of such difficulties, programmers frequently ask about them in on-line fora. Our goal is to gain some insight into both the misuse of permissions and the discussions of permissions in on-line fora. We analyze about 10,000 free apps from popular Android markets and found a significant sub-linear relationship between the popularity of a permission and the number of times when it is misused. We also study the relationship of permission use and the number of questions about the permission on StackOverflow. Finally, we study the effect of the influence of a permission (the functionality that it controls) and the interference of a permission (the number of other permissions that influence the same classes) on the occurrence of both permission misuse and permission discussions in StackOverflow.

Online advertisements (ads) provide a powerful mechanism for advertisers to effectively target Web users. Ads can be customized based on a user's browsing behavior, geographic location, and personal interests. There is currently a multi-billion dollar market for online advertising, which generates the primary revenue for some of the most popular websites on the Internet. In order to meet the immense market demand, and to manage the complex relationships between advertisers and publishers (i.e., the websites hosting the ads), marketplaces known as "ad exchanges" are employed. These exchanges allow publishers (sellers of ad space) and advertisers(buyers of this ad space) to dynamically broker traffic through ad networks to efficiently maximize profits for all parties. Unfortunately, the complexities of these systems invite a considerable amount of abuse from cybercriminals, who profit at the expense of the advertisers.