Privacy has become a significant concern in modern society as personal information about individuals is increasingly collected, used, and shared, often using digital technologies, by a wide range of organizations. To mitigate privacy concerns, organizations are required to respect privacy laws in regulated sectors (e.g., HIPAA in healthcare, GLBA in financial sector) and to adhere to self-declaredprivacy policies in self-regulated sectors (e.g., privacy policies of companies such as Google and Facebook in Web services). We investigate the possibility of formalizing and enforcing such practical privacy policies using computational techniques. We formalize privacy policies that prescribe and proscribe *flows* of personal information as well as those that place restrictions on the *purposes* for which a governed entity may use personal information. Recognizing that traditional preventive access control and information flow control mechanisms are inadequate for enforcing such privacy policies, we develop principled audit and accountability mechanisms with provable properties that seek to encourage policy-compliant behavior by detecting policy violations, assigning blame and punishing violators. We apply these techniques to several US privacy laws and organizational privacy policies, in particular, producing the first complete logical specification and audit of all disclosure-related clauses of the HIPAA Privacy Rule.