Online advertising stands as one of the largest and most successful commercial network systems, connecting millions of advertisers with billions of users. This dynamic ecosystem facilitates targeted advertising for businesses while allowing users to access a diverse array of free content on the web. Despite its significant business success, the online advertising system is undergoing substantial changes driven by increased demands and new regulations emphasizing privacy and transparency. Given these transformative shifts, we want to ask the question: what are the desired properties for the next generation online advertising system, and can we construct systems that offer both robust privacy guarantees and high efficiency? In this dissertation, we propose improvements for the current advertising system with three key properties: privacy, accountability, and anti-fraud. Specifically, we build three systems – Ibex, Addax, and Oryx, to address these challenges. Ibex is an advertising system that reduces the amount of data that is collected on users while still allowing advertisers to bid on real-time ad auctions and measure the effectiveness of their ad campaigns. Specifically, Ibex addresses an issue in recent proposals such as Google’s Privacy Sandbox Topics API in which browsers send information about topics that are of interest to a user to advertisers and demand- side platforms (DSPs). And DSPs use this information to (1) determine how much to bid on the auction for a user who is interested in particular topics, and (2) measure how well their ad campaign does for a given audience (i.e., measure conversions). Addax is a fast, verifiable, and private online ad exchange. When a user visits an ad-supported site, Addax runs an auction similar to those of leading exchanges; Addax requests bids, selects the winner, collects payment, and displays the ad to the user. A key distinction is that bids in Addax’s auctions are kept private and the outcome of the auction is publicly verifiable. Oryx is a system for efficiently detecting cycles in federated graphs where parts of the graph are held by different parties and are private. Cycle detection is an important building block in designing fraud detection algorithms that operate on confidential transaction data held by different financial institutions. Oryx allows detecting cycles of various length while keeping the topology of the graphs secret, and it does so efficiently; Oryx achieves quasilinear computational complexity and scales well with more machines with a parallel design.