arXiv subjects

Cesare Gerolimetto Fabrello

Publications and source records attributed to Cesare Gerolimetto Fabrello.

3 recordsLinked to original sources

arXiv2026

Detection and Resolution of Periodic Artifacts in OpenDP's Discrete Laplace Sampler

Differential privacy implementations rely on precise sampling from noise distributions to provide formal privacy guarantees. We report the discovery of systematic artifacts in OpenDP's discrete Laplace sampler that manifest as periodic distortions in the output distribution. Through systematic testing, we trace these artifacts to a faulty implementation in the rational arithmetic library used by the bernoulli_exp1 function, a low-level primitive that implements sampling from Bernoulli(e^(-x)) distributions. We present a diagnostic methodology that isolates the faulty component in the nested sampling hierarchy and propose an alternative implementation based on exact rational arithmetic that eliminates the artifacts. Statistical validation with 10^6 samples confirms that the corrected sampler produces outputs indistinguishable from the theoretical distribution at the tested precision level.

cs.CR↗

arXiv2026

Empirical Analysis of Randomness Quality in Differential Privacy Mechanisms

Differential Privacy (DP) relies on carefully calibrated random noise to protect individual privacy in statistical analyses. While theoretical work has analyzed DP under weakened randomness assumptions, the practical consequences of entropy degradation remain poorly understood. We present a systematic empirical investigation of how randomness quality affects differential privacy mechanisms using IBM's DiffPrivLib. We introduce progressively degraded entropy sources characterized by established test suites, starting from high-quality quantum True Random Number Generators (TRNGs) and cryptographically secure Pseudo-Random Number Generators (PRNGs) down to systematically manipulated sources with controlled entropy degradation. Through repeated experiments over one million queries on a reference database and complementary statistical tests, we directly analyze empirical Privacy Loss Random Variable distributions. Our results demonstrate that DP mechanisms reliably detect deviations when approximately 1 bit in every 8 to 16 is manipulated, with detection sensitivity varying significantly between bit-level biases and temporal correlations. We demonstrate that statistical detection of distributional anomalies does not necessarily correspond to actual privacy guarantee violations.

cs.CR↗

arXiv2026

On-line Anomaly Detection and Qualification of Random Bit Streams

Generating random bit streams is required in various applications, most notably cyber-security. Ensuring high-quality and robust randomness is crucial to mitigate risks associated with predictability and system compromise. True random numbers provide the highest unpredictability levels. However, potential biases in the processes exploited for the random number generation must be carefully monitored. This paper reports the implementation and characterization of an on-line procedure for the detection of anomalies in a true random bit stream. It is based on the NIST Adaptive Proportion and Repetition Count tests, complemented by statistical analysis relying on the Monobit and RUNS. The procedure is firmware implemented and performed simultaneously with the bit stream generation, and providing as well an estimate of the entropy of the source. The experimental validation of the approach is performed upon the bit streams generated by a quantum, silicon-based entropy source.

cs.CR↗