distributedMachineLearning/modelAggregate.oc at master · owenmx/distributedMachineLearning · GitHub

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#include "modelAggregate.oh"

void aggregate(void* args)
{
  protocolIO *io = args;
  obliv int64_t beta_avg[D], random_vals[D], n_min, sizes[M], b, sign, res, res2;
  obig randval, logval, randval2, logval2;
  int64_t prec, prec2, val, gates;

  obig_init(&randval, MAXN);
  obig_init(&logval, MAXN);
  obig_init(&randval2, MAXN);
  obig_init(&logval2, MAXN);

  for(int i = 0; i < M; i++) {
    sizes[i] = feedOblivLLong(io->sizes[i], 1);
  }
  n_min = sizes[0];
  for(int i = 1; i < M; i++) {
    obliv if (n_min > sizes[i]) {
      n_min = sizes[i];
    }
  }

  for(int j = 0; j < D; j++)
  {
    beta_avg[j] = 0;
    random_vals[j] = 0;
    for(int i = 0; i < M; i++) {
      beta_avg[j] += (feedOblivLLong(io->beta1[i][j], 1) ^ feedOblivLLong(io->beta2[i][j], 2));
      random_vals[j] ^= feedOblivLLong(io->random_vals[i][j], 1);
    }
    beta_avg[j] /= M;
  }

  b = 2 * SCALE / (M * n_min * io->lambda * io->epsilon);

////////// lap(b) - (+/-) * log(random_vals) * b; b = 2 * SCALE / (m * n * lambda * epsilon); random_vals should be in (0,1]

  obig_import_onative(&randval2, RAND_MAX);
  ofixed_ln(&logval2, &prec2, randval2, 0);
  res2 = obig_export_onative_signed(logval2);
  res2 /= (1LL << prec2);

  for(int j = 0; j < D; j++) {
    sign = 1;
    obliv if (random_vals[j] < 0) {
      sign = -1;
    }

    obig_import_onative(&randval, random_vals[j]);
    gates = yaoGateCount();
    ofixed_ln(&logval, &prec, randval, 0);
    gates = yaoGateCount() - gates;
    res = obig_export_onative_signed(logval);
    res /= (1LL << prec);
    fprintf(stderr, "Log gate count: %llu\n", gates);
    beta_avg[j] += (res - res2) * b * sign;
  }

  printGateCount();

  for(int j = 0; j < D; j++) {
    revealOblivLLong(&io->beta_avg[j], beta_avg[j], 0);
  }

  obig_free(&randval);
  obig_free(&logval);
  obig_free(&randval2);
  obig_free(&logval2);
}