Differential privacy for aggregates - add dp_laplace and dp_gaussian built-in functions with budget accounting

[andersonm-ibm]

• Add two native DML built-ins for differentially private aggregate release "colMeans", "colSums" or "identity":

result = dp_laplace(X, "colMeans", sensitivity, epsilon)
result = dp_gaussian(X, "colSums", sensitivity, epsilon, delta)

• Wire them through the full compilation pipeline:
  Builtins → BuiltinFunctionExpression → ParameterizedBuiltinOp HOP → ParameterizedBuiltin LOP → DPBuiltinCPInstruction.

• Introduce DPBudgetAccountant, a session-scoped privacy budget tracker stored on ExecutionContext. Laplace releases use exact pure-ε composition; Gaussian releases use Rényi DP composition (Mironov 2017) with RDP → (ε,δ) conversion for tighter bounds. Raises DMLRuntimeException if cumulative spend exceeds the budget.

• Unit tests covering constructor validation, Laplace/Gaussian composition, budget exhaustion for both mechanisms, mixed composition, release counting, and RDP mathematical invariants (sensitivity cancellation, ε-monotonicity).

• End-to-end DML integration tests in DPBuiltinDMLTest verify noisy output differs from clean means by a statistically plausible amount.

• Differential Privacy Benchmark:
  Four federated workers simulated on localhost, a logistic regression FedAvg loop in DML where the coordinator applies dp_gaussian to the aggregated gradient, a sweep over ε ∈ {0.5, 1, 4, 8} plus a non-private baseline, and a matplotlib accuracy-vs-ε plot saved as a PNG.

CC @ywcb00