Biblio

According to the theory of contextual integrity (CI), privacy norms prescribeinformation flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), incontextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by IT, CI was able both to pinpoint sources of disruption andprovide grounds for either accepting or rejecting them. Mounting challengesfrom a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces themetaphor of a data (food) chain to capture the nature of these challenges.With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely.

Modern enterprises rely on Data Leakage Prevention (DLP) systems to enforce privacy policies that prevent unintentional flow of sensitive information to unauthorized entities. However, these systems operate based on rule sets that are limited to syntactic analysis and therefore completely ignore the semantic relationships between participants involved in the information exchanges. For similar reasons, these systems cannot enforce complex privacy policies that require temporal reasoning about events that have previously occurred. 

To address these limitations, we advocate a new design methodology for DLP systems centered on the notion of Contextual Integrity (CI). We use the CI framework to abstract real-world communication exchanges into formally defined information flows where privacy policies describe sequences of admissible flows. CI allows us to decouple (1) the syntactic extraction of flows from information exchanges, and (2) the enforcement of privacy policies on these flows. We applied this approach to built VACCINE, a DLP auditing system for emails. VACCINE uses state-of-the-art techniques in natural language processing to extract flows from email text. It also provides a declarative language for describing privacy policies. These policies are automatically compiled to operational rules that the system uses for detecting data leakages. We evaluated VACCINE on the Enron email corpus and show that it improves over the state of the art both in terms of the expressivity of the policies that DLP systems can enforce as well as its precision in detecting data leakages.