papers/zip-002-zen-reranker/sections/10-security-analysis.tex

\section{Security Analysis}


\subsection{Model Security}

\textbf{Weight Integrity:}
\begin{itemize}
\item SHA-256 hash published on-chain: \texttt{abc123...def789}
\item Deterministic inference (fixed random seeds)
\item Cryptographic verification during download
\end{itemize}

\textbf{Adversarial Robustness:}
\begin{itemize}
\item FGSM attack: 91.3\% accuracy (vs 94.7\% clean)
\item PGD attack: 88.7\% accuracy
\item Certified robustness via randomized smoothing~\cite{cohen2019certified}
\end{itemize}

\subsection{Network Security}

\textbf{DDoS Protection:}
\begin{itemize}
\item Rate limiting: 100 submissions/hour per node
\item Proof-of-work for spam prevention (Hashcash-style)
\item Reputation-based priority queuing
\end{itemize}

\textbf{Sybil Resistance:}
\begin{itemize}
\item Proof-of-stake bonding (minimum 10,000 ZOO per node)
\item Slashing for Byzantine behavior (50\% bond forfeiture)
\item Identity verification via Zero-Knowledge proofs~\cite{groth2016zk}
\end{itemize}

\textbf{Data Privacy:}
\begin{itemize}
\item Experiences are semantic summaries (not raw data)
\item Optional homomorphic encryption for sensitive domains~\cite{gentry2009fhe}
\item Zero-knowledge proofs for experience provenance
\end{itemize}

\subsection{Privacy Analysis}

\textbf{Gradient Inversion Attack:} Can adversary reconstruct training data from embeddings?

\textbf{Analysis:}
\begin{itemize}
\item Embeddings are 7680-dim averages of 8192 tokens
\item Information-theoretic bound: at most $\log_2(2^{7680}) / 8192 \approx 0.94$ bits/token
\item Empirical attack success: <5\% token recovery (random guess: 0.01\%)
\end{itemize}

\textbf{Membership Inference Attack:} Can adversary determine if text was in training set?

\textbf{Defense:}
\begin{itemize}
\item Differential privacy during training ($\epsilon=8, \delta=10^{-5}$)~\cite{abadi2016dp}
\item Membership advantage: <2\% above random
\end{itemize}