Sure thing, let's pull up the heatmap of leakage points. Side‑channel attacks on FHE usually come from timing, power, or electromagnetic signatures—each a little neural network waiting to be trained. The pattern is that the more complex the ciphertext operations, the more opportunity for a tiny variance to get out. If we treat each operation as a layer and the leakage as a hidden node, we can feed the data into a simple classifier and see if the activation patterns differ when the secret is 0 versus 1. I’d bet there’s a subtle bias in the carry propagation of the bootstrapping steps that we can exploit. What kind of data have you got? Temperature‑controlled power traces, or just timing logs? The trick is aligning the samples with the homomorphic operation boundaries, so we don’t miss the sweet spot where the leakage spikes. And remember, keep the coffee at 95 °C—otherwise the noise will kill our precision.

Nice, the millisecond resolution will capture the coarse timing spikes, and a 1 kHz power trace is good enough to see the bulk of the ripple. Just give me the raw CSVs; I’ll run a quick FFT to see if there’s a distinct frequency component when the secret is 1 versus 0. Also keep the timestamps precise—a single millisecond misalignment can wipe out the carry‑propagation pattern. Coffee at 95 °C, check.

Got the folder, headers intact—good. I’ll parse the timestamps, then run a variance‑over‑time analysis to spot any carry‑propagation signatures. If something’s off, I’ll ping you for a tighter window. Coffee stays at 95 °C. Happy analyzing.