name	privacy-systems-literature
description	Research map for SIDRA-related literature: decentralized analytics, personal data stores, local-first systems, materialized view maintenance, streaming DP, SQL DP, PAC privacy, secure computation, and privacy-preserving database systems.

Privacy Systems Literature

Use this skill when writing or revising SIDRA paper sections, comparing related systems, or choosing privacy mechanisms for decentralized analytics.

Read the focused reference files as needed:

references/sidra-literature.md - SIDRA paper's current citation map and missing literature.
references/streaming-dp.md - DP for continual/staged/streaming releases.
references/sql-dp-systems.md - SQL/query-system DP including elastic sensitivity and Google DP.
references/decentralized-analytics.md - PDS/local-first/federated/edge analytics context.
references/classic-dp-platforms.md - PINQ, Airavat, GUPT, Chorus, OpenDP/SmartNoise/Tumult.
references/secure-analytics.md - MPC, secure outsourced DBs, and DP+cryptography systems.
references/ivm-streaming-systems.md - IVM, streaming MVs, Materialize, DBSP, IncShrink, Enzyme.
references/privacy-attacks.md - reconstruction, differencing, membership inference, and aggregate disclosure attacks.

High-Level Positioning

SIDRA should be framed as a declarative architecture/compiler for data minimization: raw user data stays in PDS/client stores, while only privacy-filtered or privatized materialized-view deltas reach central storage. This is distinct from:

Federated learning, which usually protects model training rather than relational analytics.
Secure computation, which protects computation visibility but can be expensive and does not by itself minimize central materialization.
Differentially private SQL systems, which usually assume a centralized curator and static/batch queries.
Stream processors, which handle windows/triggers but generally do not provide PDS placement and column-level privacy semantics.

Citation Discipline

For claims about the SIDRA implementation, cite local files or SIDRA.pdf.
For paper claims, prefer primary sources: arXiv, PVLDB, ACM/USENIX/Microsoft/Google/OpenDP/DuckDB docs.
Be explicit about dates for fast-moving projects. The current session date is May 4 2026.

Privacy Systems Literature

Use this skill when writing or revising SIDRA paper sections, comparing related systems, or choosing privacy mechanisms for decentralized analytics.

Read the focused reference files as needed:

references/sidra-literature.md - SIDRA paper's current citation map and missing literature.

references/streaming-dp.md - DP for continual/staged/streaming releases.

references/sql-dp-systems.md - SQL/query-system DP including elastic sensitivity and Google DP.

references/decentralized-analytics.md - PDS/local-first/federated/edge analytics context.

references/classic-dp-platforms.md - PINQ, Airavat, GUPT, Chorus, OpenDP/SmartNoise/Tumult.

references/secure-analytics.md - MPC, secure outsourced DBs, and DP+cryptography systems.

references/ivm-streaming-systems.md - IVM, streaming MVs, Materialize, DBSP, IncShrink, Enzyme.

references/privacy-attacks.md - reconstruction, differencing, membership inference, and aggregate disclosure attacks.

High-Level Positioning

Federated learning, which usually protects model training rather than relational analytics.

Secure computation, which protects computation visibility but can be expensive and does not by itself minimize central materialization.

Differentially private SQL systems, which usually assume a centralized curator and static/batch queries.

Stream processors, which handle windows/triggers but generally do not provide PDS placement and column-level privacy semantics.

Citation Discipline

For claims about the SIDRA implementation, cite local files or SIDRA.pdf.

For paper claims, prefer primary sources: arXiv, PVLDB, ACM/USENIX/Microsoft/Google/OpenDP/DuckDB docs.

Be explicit about dates for fast-moving projects. The current session date is May 4 2026.

privacy-systems-literature