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Restructured from flat .tex/.pdf files into per-paper subdirectories. Added Antje Worring as co-author on all papers. Synced PDFs to pdfs/ for download site.
50 lines
2.1 KiB
TeX
50 lines
2.1 KiB
TeX
\section{Migration Path}
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\label{sec:migration}
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% ============================================================================
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Existing applications built on Firebase, Supabase, Notion APIs, or similar
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centralized backends can migrate to capsule-based architectures incrementally.
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\subsection{Migration Mapping}
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\begin{table}[H]
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\centering
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\caption{Migration from centralized backends to capsule architecture.}
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\label{tab:migration}
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\begin{tabular}{@{}lll@{}}
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\toprule
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\textbf{Centralized} & \textbf{Capsule Equivalent} & \textbf{Trust Model} \\
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\midrule
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Firebase Auth & I-Chain DID + session tokens & Self-sovereign \\
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Firestore & Encrypted SQLite vault & Local-first \\
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Realtime DB & CRDT sync over $\Sigma$ & Peer-to-peer \\
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Cloud Storage & Encrypted blob at provider & Provider-blind \\
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Cloud Functions & Base plugins (WASM/V8) & Sandboxed \\
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Security Rules & $\Gamma$ capability lattice & Cryptographic \\
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Push notifications & Sync event subscriptions & Local observer \\
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Analytics & Aggregation via Class III FHE & Confidential \\
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\bottomrule
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\end{tabular}
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\end{table}
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\subsection{Migration Strategy}
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\begin{enumerate}[nosep]
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\item \textbf{Phase 1: Dual-write.} The application writes to both the
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existing backend and a local capsule vault. Reads come from the
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centralized backend. This validates data model compatibility.
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\item \textbf{Phase 2: Local-read.} Reads switch to the local vault.
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The centralized backend becomes a sync target (write-through).
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The user experiences offline capability and reduced latency.
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\item \textbf{Phase 3: Cut-over.} The centralized backend is removed from
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$\Sigma$. The capsule's CRDT sync handles replication. Identity
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migrates to I-Chain. The old backend is decommissioned.
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\end{enumerate}
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The key constraint: the application's data model must fit SQLite's relational
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schema. Document databases (Firestore, MongoDB) require schema normalization.
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Key-value stores map directly. Graph data requires an adjacency-list
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representation or a dedicated CRDT type.
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% ============================================================================
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