This dissertation focuses on the design and analysis of accountable computing for a wide range of networked systems with affordable expense. The central idea is to incorporate accountability, a long-neglected security objective, into the design and implementation of modern computing systems. Broadly speaking, accountability in the cyber-security domain means that every entity ought to be held responsible for its behavior, and that there always exists undeniable and verifiable evidence linking each event to the liable entities. This dissertation studies accountable computing in three different contexts, including traditional distributed systems, cloud computing, and the Smart Grid. We first propose a quantitative model called P-Accountability to assess the degree of system accountability. P-Accountability consists of a flat model and a hierarchical model. Our results show that P-Accountability is an effective metric to evaluate general distributed systems such as PeerReview [1] in terms of accountability. Next, we develop Accountable MapReduce for cloud computing to prevent malicious working machines from manipulating the processing results. To achieve this goal, we set up a group of auditors to perform an Accountability-Test (A-test) that checks all working machines and detects malicious nodes in real time. Finally, we investigate the accountability issues in the neighborhood area smart grid. A mutual inspection scheme is presented to enable non-repudiation for metering. In addition, we propose and analyze a suite of algorithms to identify malicious meters for the detection of energy theft.