This paper takes us away from looking at analytics as an attack on privacy. In fact, this paper will refractor the way that we think. This will allow for personal data stores and personal web observatories that keep the data in user-controlled nodes. At the same time, in-situ computation, k-anonymous aggregates, and differentially private insights will happen without hoarding data centrally. The peer exchange of architecture details is authenticated and encrypted. Replication and durability are inspired by LOCKSS. Synchronization with conflict resolution will allow longitudinal personal datasets to persist under changing threat and compliance regimes. A reference” atom” shows that we can use policy-guided exposure, anonymous distributed query, and selective queries to reduce the risk of linkage. It also establishes the governance for identifiable information and sensitive categories. The model moves from observatories centered on aggregators to a federated substructure that protects privacy. This helps to discover surfaces, regulatory latticework, and security, consentful data sharing at scale.