The success of platforms such as Facebook and Google has been due in no small part to features that allow advertisers to target ads in a fine-grained manner. However, these features open up the potential for discriminatory advertising when advertisers include or exclude users of protected classes---either directly or indirectly---in a discriminatory fashion. Despite the fact that advertisers are able to compose various targeting features together, the existing mitigations to discriminatory targeting have focused only on individual features; there are concerns that such composition could result in targeting that is more discriminatory than the features individually.

Data brokers such as Acxiom and Experian are in the business of collecting and selling data on people; the data they sell is commonly used to feed marketing as well as political campaigns. Despite the ongoing privacy debate, there is still very limited visibility into data collection by data brokers. Recently, however, online advertising services such as Facebook have begun to partner with data brokers-to add additional targeting features to their platform- providing avenues to gain insight into data broker information.

Online advertising platforms such as those of Facebook and Google collect detailed data about users, which they leverage to allow advertisers to target ads to users based on various pieces of user information. While most advertising platforms have transparency mechanisms in place to reveal this collected information to users, these often present an incomplete view of the information being collected and of how it is used for targeting ads, thus necessitating further transparency.

Sites like Facebook and Google now serve as de facto data brokers, aggregating data on users for the purpose of implementing powerful advertising platforms. Historically, these services allowed advertisers to select which users see their ads via targeting attributes. Recently, most advertising platforms have begun allowing advertisers to target users directly by uploading the personal information of the users who they wish to advertise to (e.g., their names, email addresses, phone numbers, etc.); these services are often known as custom audiences. Custom audiences effectively represent powerful linking mechanisms, allowing advertisers to leverage any PII (e.g., from customer data, public records, etc.) to target users. In this paper, we focus on Facebook's custom audience implementation and demonstrate attacks that allow an adversary to exploit the interface to infer users' PII as well as to infer their activity. Specifically, we show how the adversary can infer users' full phone numbers knowing just their email address, determine whether a particular user visited a website, and de-anonymize all the visitors to a website by inferring their phone numbers en masse. These attacks can be conducted without any interaction with the victim(s), cannot be detected by the victim(s), and do not require the adversary to spend money or actually place an ad. We propose a simple and effective fix to the attacks based on reworking the way Facebook de-duplicates uploaded information. Facebook's security team acknowledged the vulnerability and has put into place a fix that is a variant of the fix we propose. Overall, our results indicate that advertising platforms need to carefully consider the privacy implications of their interfaces.

On most current websites untrustworthy or spammy identities are easily created. Existing proposals to detect untrustworthy identities rely on reputation signals obtained by observing the activities of identities over time within a single site or domain; thus, there is a time lag before which websites cannot easily distinguish attackers and legitimate users. In this paper, we investigate the feasibility of leveraging information about identities that is aggregated across multiple domains to reason about their trustworthiness. Our key insight is that while honest users naturally maintain identities across multiple domains (where they have proven their trustworthiness and have acquired reputation over time), attackers are discouraged by the additional effort and costs to do the same. We propose a flexible framework to transfer trust between domains that can be implemented in today's systems without significant loss of privacy or significant implementation overheads.

People have long been aware of malicious users that impersonate celebrities or launch identity theft attacks in social networks. However, beyond anecdotal evidence, there have been no in-depth studies of impersonation attacks in today's social networks. One reason for the lack of studies in this space is the absence of datasets about impersonation attacks. To this end, we propose a technique to build extensive datasets of impersonation attacks in current social networks and we gather 16,572 cases of impersonation attacks in the Twitter social network. Our analysis reveals that most identity impersonation attacks are not targeting celebrities or identity theft. Instead, we uncover a new class of impersonation attacks that clone the profiles of ordinary people on Twitter to create real-looking fake identities and use them in malicious activities such as follower fraud. We refer to these as the doppelgänger bot attacks. Our findings show (i) that identity impersonation attacks are much broader than believed and can impact any user, not just celebrities and (ii) that attackers are evolving and create real-looking accounts that are harder to detect by current systems. We also propose and evaluate methods to automatically detect impersonation attacks sooner than they are being detected in today's Twitter social network.

Many-to-Many (M2M) communication, which allows a group of nodes to communicate with each other simultaneously, has been shown to help battle contention for scarce resources in Delay Tolerant Networks (DTNs), and hence improve their performance. In this paper, we address for the first time the problem of joint message scheduling and drop in a DTN which employs M2M communication. We develop a generic framework, assuming the routing algorithm provides us with a utility function (derived in order to optimize some desired metric) which helps us compute utilities associated with each message. The goal of our joint message scheduling and drop framework is to maximize the increase in total utility. We formulate the problem of joint message scheduling and drop in DTNs that employ M2M communication as an integer optimization problem and show how to reduce it into a number of many-to-one problems. In order to solve the many-to-one integer optimization problems obtained, we develop an efficient but 1/2 -approximate greedy algorithm (Almost-Greedy), and use it to develop an optimal solution (DynOpt). Finally, we use DynOpt to develop an (1 - ε)- approximate Fully Polynomial Time Approximation Scheme (eOpt) that lets us choose a tradeoffs between complexity and accuracy. The problem of joint message scheduling and drop in DTNs employing the traditional one-to-one communication is a special case of our problem where the communicating group size is limited to 2 and hence our contribution provides a theoretical formulation and theoretically proven solutions for that problem too. We compare our algorithms against adaptations of the state of the art one-to-one scheduling and drop policies to the M2M communication case using simulations and find that our algorithms significantly improve the performance of the network.