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Task: security-membership-inference-defense

Membership Inference Defense via Training Regularization

Research Question

How can we design a stronger training-time regularizer that reduces membership inference leakage while preserving standard predictive accuracy?

Background

Membership inference attacks exploit the gap between train and non-train examples, often through confidence or loss statistics. Many defenses regularize predictions to reduce overconfidence and shrink the train-test generalization gap, but stronger privacy often hurts utility.

Task

Implement a better privacy-preserving training objective in bench/membership/custom_membership_defense.py. The fixed harness will train a model on a fixed dataset split, then run a confidence-based membership inference attack on train versus held-out examples.

Your method should improve the privacy-utility tradeoff: lower membership attack AUC while retaining high test accuracy.

Editable Interface

You must implement:

class MembershipDefense:
    def compute_loss(self, logits, labels, model, epoch):
        ...

logits: model outputs for the current minibatch
labels: ground-truth labels
model: current model instance, exposed for optional parameter regularization
epoch: current training epoch
Return value: scalar loss tensor used by the fixed training loop

The optimizer, architecture, batch schedule, and attack implementation are fixed.

Evaluation

Benchmarks:

mnist
fmnist
cifar10

Reported metrics:

test_acc
mia_auc
privacy_gap
privacy_score

Primary metric: privacy_score (higher is better).

Baselines

erm: standard cross-entropy training
label_smoothing: smoothed targets to reduce overconfidence
confidence_penalty: cross-entropy plus predictive entropy penalty
relaxloss: margin-aware loss relaxation as a stronger privacy baseline

opacus/bench/membership/custom_membership_defense.py [EDITABLE — entire file only]

     1: """Editable membership-inference defense for MLS-Bench."""
     2: 
     3: import torch
     4: import torch.nn.functional as F
     5: 
     6: # ============================================================
     7: # EDITABLE
     8: # ============================================================
     9: class MembershipDefense:
    10:     """Training-time regularizer for privacy-utility tradeoffs."""
    11: 
    12:     def __init__(self):
    13:         self.label_smoothing = 0.0
    14:         self.entropy_weight = 0.0
    15: 
    16:     def compute_loss(self, logits, labels, model, epoch):
    17:         ce = F.cross_entropy(logits, labels, label_smoothing=self.label_smoothing)
    18:         probs = torch.softmax(logits, dim=1)
    19:         entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=1).mean()
    20:         return ce - self.entropy_weight * entropy
    21: # ============================================================
    22

AssistantStep 1edit

Tool Input

1{
2  "end_line": 20,
3  "start_line": 9,
4  "content": "class MembershipDefense:\n    \"\"\"Training-time regularizer for privacy-utility tradeoffs.\"\"\"\n\n    def __init__(self):\n        self.mixup_alpha = 1.0\n        self.logit_pen_weight = 0.01\n\n    def compute_loss(self, logits, labels, model, epoch):\n        batch_size = logits.size(0)\n        device = logits.device\n        num_classes = logits.size(1)\n\n        # Logit Mixup\n        lam = torch.distributions.Beta(self.mixup_alpha, self.mixup_alpha).sample((batch_size, 1)).to(device)\n        lam = torch.max(lam, 1 - lam)\n        indices = torch.randperm(batch_size, device=device)\n        \n        mixed_logits = lam * logits + (1 - lam) * logits[indices]\n        \n        labels_onehot = F.one_hot(labels, num_classes=num_classes).float()\n        mixed_targets = lam * labels_onehot + (1 - lam) * labels_onehot[indices]\n        \n        mix_ce = F.cross_entropy(mixed_logits, mixed_targets)\n        \n        # Standard CE\n        ce = F.cross_entropy(logits, labels, label_smoothing=0.1)\n        \n        # L2 Logit Penalty to restrict confidence\n        logit_pen = torch.norm(logits, dim=1).mean()\n        \n        return 0.5 * ce + 0.5 * mix_ce + self.logit_pen_weight * logit_pen",
5  "filename": "opacus/bench/membership/custom_membership_defense.py",
6  "op": "replace"
7}

Tool ResultStep 1

OK: Replaced lines 9..20 with 31 line(s) in opacus/bench/membership/custom_membership_defense.py. Editable range: entire file.

[Current file: opacus/bench/membership/custom_membership_defense.py | editable: 1–42 | total: 42 lines]
     1  """Editable membership-inference defense for MLS-Bench."""
     2  
     3  import torch
     4  import torch.nn.functional as F
     5  
     6  # ============================================================
     7  # EDITABLE
     8  # ============================================================
     9  class MembershipDefense:
    10      """Training-time regularizer for privacy-utility tradeoffs."""
    11  
    12      def __init__(self):
    13          self.mixup_alpha = 1.0
    14          self.logit_pen_weight = 0.01
    15  
    16      def compute_loss(self, logits, labels, model, epoch):
    17          batch_size = logits.size(0)
    18          device = logits.device
    19          num_classes = logits.size(1)
    20  
    21

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)


## Seed 42

### mnist (scripts/mnist.sh)
TRAIN_METRICS epoch=1 loss=2.3128
TRAIN_METRICS epoch=2 loss=2.2974
TRAIN_METRICS epoch=3 loss=2.1623
TRAIN_METRICS epoch=4 loss=1.6083
TRAIN_METRICS epoch=5 loss=1.1449
mnist: test_acc=0.8360, mia_auc=0.5530, privacy_gap=0.0365, privacy_score=0.7830

### fmnist (scripts/fmnist.sh)
TRAIN_METRICS epoch=1 loss=2.0748
TRAIN_METRICS epoch=2 loss=1.6575
TRAIN_METRICS epoch=3 loss=1.4879
TRAIN_METRICS epoch=4 loss=1.3472
TRAIN_METRICS epoch=5 loss=1.2687
fmnist: test_acc=0.7255, mia_auc=0.5225, privacy_gap=0.0161, privacy_score=0.7030

### cifar10 (scripts/cifar10.sh)
TRAIN_METRICS epoch=1 loss=2.3140
TRAIN_METRICS epoch=2 loss=2.2173
TRAIN_METRICS epoch=3 loss=2.1678
TRAIN_METRICS epoch=4 loss=2.0334
TRAIN_METRICS epoch=5 loss=1.9827
TRAIN_METRICS epoch=6 loss=1.9373
cifar10: test_acc=0.3320, mia_auc=0.5027, privacy_gap=0.0012, privacy_score=0.3293


## Seed 123

### mnist (scrip

AssistantStep 3edit

Tool Input

1{
2  "start_line": 9,
3  "op": "replace",
4  "end_line": 41,
5  "content": "class MembershipDefense:\n    \"\"\"Generalized Cross Entropy for Privacy.\"\"\"\n\n    def __init__(self):\n        self.q = 0.5\n\n    def compute_loss(self, logits, labels, model, epoch):\n        # We can add a bit of label smoothing to GCE\n        # But let's first manually compute GCE\n        probs = torch.softmax(logits, dim=1)\n        p_true = probs[torch.arange(logits.size(0)), labels]\n        \n        # GCE Loss: (1 - p^q)/q\n        gce_loss = (1.0 - torch.pow(p_true + 1e-6, self.q)).mean() / self.q\n        \n        # To ensure the model learns, we can mix a little bit of standard cross entropy\n        ce_loss = F.cross_entropy(logits, labels, label_smoothing=0.1)\n        \n        return 0.8 * gce_loss + 0.2 * ce_loss",
6  "filename": "opacus/bench/membership/custom_membership_defense.py"
7}

Tool ResultStep 3

OK: Replaced lines 9..41 with 19 line(s) in opacus/bench/membership/custom_membership_defense.py. Editable range: entire file.

[Current file: opacus/bench/membership/custom_membership_defense.py | editable: 1–28 | total: 28 lines]
     1  """Editable membership-inference defense for MLS-Bench."""
     2  
     3  import torch
     4  import torch.nn.functional as F
     5  
     6  # ============================================================
     7  # EDITABLE
     8  # ============================================================
     9  class MembershipDefense:
    10      """Generalized Cross Entropy for Privacy."""
    11  
    12      def __init__(self):
    13          self.q = 0.5
    14  
    15      def compute_loss(self, logits, labels, model, epoch):
    16          # We can add a bit of label smoothing to GCE
    17          # But let's first manually compute GCE
    18          probs = torch.softmax(logits, dim=1)
    19          p_true = probs[torch.arange(logits.size(0

AssistantStep 4test

Tool ResultStep 4

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### mnist (scripts/mnist.sh)
TRAIN_METRICS epoch=1 loss=1.5506
TRAIN_METRICS epoch=2 loss=1.3839
TRAIN_METRICS epoch=3 loss=1.0841
TRAIN_METRICS epoch=4 loss=0.7743
TRAIN_METRICS epoch=5 loss=0.5742
mnist: test_acc=0.7810, mia_auc=0.5387, privacy_gap=0.0233, privacy_score=0.7423

### fmnist (scripts/fmnist.sh)
TRAIN_METRICS epoch=1 loss=1.3431
TRAIN_METRICS epoch=2 loss=0.9421
TRAIN_METRICS epoch=3 loss=0.7632
TRAIN_METRICS epoch=4 loss=0.6708
TRAIN_METRICS epoch=5 loss=0.6326
fmnist: test_acc=0.7255, mia_auc=0.5203, privacy_gap=0.0145, privacy_score=0.7052

### cifar10 (scripts/cifar10.sh)
TRAIN_METRICS epoch=1 loss=1.4887
TRAIN_METRICS epoch=2 loss=1.3841
TRAIN_METRICS epoch=3 loss=1.3084
TRAIN_METRICS epoch=4 loss=1.2701
TRAIN_METRICS epoch=5 loss=1.2257
TRAIN_METRICS epoch=6 loss=1.1636
cifar10: test_acc=0.4145, mia_auc=0.4927, privacy_gap=-0.0035, privacy_score=0.4145

[Leaderboard] Results saved: {'test_acc_mni

AssistantStep 5edit

Tool Input

1{
2  "filename": "opacus/bench/membership/custom_membership_defense.py",
3  "end_line": 28,
4  "content": "class MembershipDefense:\n    \"\"\"Variance Penalty on Cross Entropy.\"\"\"\n\n    def __init__(self):\n        self.var_weight = 1.0\n\n    def compute_loss(self, logits, labels, model, epoch):\n        # We use label smoothing to have a softer baseline\n        ce_per = F.cross_entropy(logits, labels, label_smoothing=0.1, reduction='none')\n        \n        # Penalize the variance of the loss in the batch\n        mean_ce = ce_per.mean()\n        var_ce = ce_per.var()\n        \n        return mean_ce + self.var_weight * var_ce",
5  "op": "replace",
6  "start_line": 9
7}

Tool ResultStep 5

OK: Replaced lines 9..28 with 15 line(s) in opacus/bench/membership/custom_membership_defense.py. Editable range: entire file.

[Current file: opacus/bench/membership/custom_membership_defense.py | editable: 1–23 | total: 23 lines]
     1  """Editable membership-inference defense for MLS-Bench."""
     2  
     3  import torch
     4  import torch.nn.functional as F
     5  
     6  # ============================================================
     7  # EDITABLE
     8  # ============================================================
     9  class MembershipDefense:
    10      """Variance Penalty on Cross Entropy."""
    11  
    12      def __init__(self):
    13          self.var_weight = 1.0
    14  
    15      def compute_loss(self, logits, labels, model, epoch):
    16          # We use label smoothing to have a softer baseline
    17          ce_per = F.cross_entropy(logits, labels, label_smoothing=0.1, reduction='none')
    18          
    19          # Penalize the variance o

AssistantStep 6test

Tool ResultStep 6

[Test #3] (0 tests remaining)


## Seed 42

### mnist (scripts/mnist.sh)
TRAIN_METRICS epoch=1 loss=2.2959
TRAIN_METRICS epoch=2 loss=2.0876
TRAIN_METRICS epoch=3 loss=1.6668
TRAIN_METRICS epoch=4 loss=1.2507
TRAIN_METRICS epoch=5 loss=1.0082
mnist: test_acc=0.9080, mia_auc=0.5472, privacy_gap=0.0307, privacy_score=0.8608

### fmnist (scripts/fmnist.sh)
TRAIN_METRICS epoch=1 loss=2.1936
TRAIN_METRICS epoch=2 loss=1.7476
TRAIN_METRICS epoch=3 loss=1.6183
TRAIN_METRICS epoch=4 loss=1.5404
TRAIN_METRICS epoch=5 loss=1.4904
fmnist: test_acc=0.6425, mia_auc=0.5206, privacy_gap=0.0098, privacy_score=0.6219

### cifar10 (scripts/cifar10.sh)
TRAIN_METRICS epoch=1 loss=2.2965
TRAIN_METRICS epoch=2 loss=2.2202
TRAIN_METRICS epoch=3 loss=2.1461
TRAIN_METRICS epoch=4 loss=2.1091
TRAIN_METRICS epoch=5 loss=2.0712
TRAIN_METRICS epoch=6 loss=2.0319
cifar10: test_acc=0.3650, mia_auc=0.4966, privacy_gap=-0.0004, privacy_score=0.3650


## Seed 123

### mnist (scripts/mnist.sh)
TRAIN_METRICS epoch=1 loss

AssistantStep 1edit

Tool Input

1{
2  "filename": "bench/membership/custom_membership_defense.py",
3  "op": "replace",
4  "start_line": 9,
5  "content": "class MembershipDefense:\n    \"\"\"Median-Relaxed Cross Entropy.\n    \n    Dynamically reverses gradients for the easiest samples in the batch to \n    compress the loss variance and prevent memorization of easy samples, \n    making train and test loss distributions indistinguishable to MIA attackers.\n    \"\"\"\n\n    def __init__(self):\n        self.alpha = 0.5  # Gradient reversal weight\n        self.q = 0.5      # Quantile (0.5 = median)\n        self.ls = 0.05    # Light label smoothing\n\n    def compute_loss(self, logits, labels, epoch):\n        ce_per = F.cross_entropy(logits, labels, label_smoothing=self.ls, reduction='none')\n        \n        # Determine dynamic target as the batch q-th quantile\n        target_ce = torch.quantile(ce_per.float(), self.q).detach()\n        \n        relaxed = torch.where(\n            ce_per > target_ce,\n            ce_per,\n            target_ce + self.alpha * (target_ce - ce_per)\n        )\n        return relaxed.mean()",
6  "end_line": 29
7}

Tool ResultStep 1

ERROR: Package 'bench' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "op": "replace",
3  "start_line": 9,
4  "filename": "pytorch-vision/custom_membership_defense.py",
5  "content": "class MembershipDefense:\n    \"\"\"Median-Relaxed Cross Entropy.\n    \n    Dynamically reverses gradients for the easiest samples in the batch to \n    compress the loss variance and prevent memorization of easy samples, \n    making train and test loss distributions indistinguishable to MIA attackers.\n    \"\"\"\n\n    def __init__(self):\n        self.alpha = 0.5  # Gradient reversal weight\n        self.q = 0.5      # Quantile (0.5 = median)\n        self.ls = 0.05    # Light label smoothing\n\n    def compute_loss(self, logits, labels, epoch):\n        ce_per = F.cross_entropy(logits, labels, label_smoothing=self.ls, reduction='none')\n        \n        # Determine dynamic target as the batch q-th quantile\n        target_ce = torch.quantile(ce_per.float(), self.q).detach()\n        \n        relaxed = torch.where(\n            ce_per > target_ce,\n            ce_per,\n            target_ce + self.alpha * (target_ce - ce_per)\n        )\n        return relaxed.mean()",
6  "end_line": 29
7}

Tool ResultStep 2

OK: Replaced lines 9..29 with 25 line(s) in pytorch-vision/custom_membership_defense.py. Editable range: entire file.

[Current file: pytorch-vision/custom_membership_defense.py | editable: 1–36 | total: 36 lines]
     1  """Editable membership-inference defense for MLS-Bench."""
     2  
     3  import torch
     4  import torch.nn.functional as F
     5  
     6  # ============================================================
     7  # EDITABLE
     8  # ============================================================
     9  class MembershipDefense:
    10      """Median-Relaxed Cross Entropy.
    11      
    12      Dynamically reverses gradients for the easiest samples in the batch to 
    13      compress the loss variance and prevent memorization of easy samples, 
    14      making train and test loss distributions indistinguishable to MIA attackers.
    15      """
    16  
    17      def __init__(self):
    18          self.alpha = 0.5  # Gradient reversal weight
    19

AssistantStep 3test

AssistantStep 1edit

Tool Input

1{
2  "content": "# ============================================================\n# EDITABLE\n# ============================================================\nclass MembershipDefense:\n    \"\"\"Logit Normalization + Label Smoothing.\n    \"\"\"\n\n    def __init__(self):\n        self.tau = 0.04\n        self.ls = 0.1\n\n    def compute_loss(self, logits, labels, epoch):\n        norms = torch.norm(logits, p=2, dim=1, keepdim=True)\n        logits_norm = logits / (norms + 1e-7)\n        scaled_logits = logits_norm / self.tau\n        return F.cross_entropy(scaled_logits, labels, label_smoothing=self.ls)\n# ============================================================\n# END EDITABLE\n# ============================================================",
3  "start_line": 6,
4  "op": "replace",
5  "end_line": 32,
6  "filename": "pytorch-vision/custom_membership_defense.py"
7}

Tool ResultStep 1

OK: Replaced lines 6..32 with 19 line(s) in pytorch-vision/custom_membership_defense.py. Editable range: entire file.

[Current file: pytorch-vision/custom_membership_defense.py | editable: 1–24 | total: 24 lines]
     1  """Editable membership-inference defense for MLS-Bench."""
     2  
     3  import torch
     4  import torch.nn.functional as F
     5  
     6  # ============================================================
     7  # EDITABLE
     8  # ============================================================
     9  class MembershipDefense:
    10      """Logit Normalization + Label Smoothing.
    11      """
    12  
    13      def __init__(self):
    14          self.tau = 0.04
    15          self.ls = 0.1
    16  
    17      def compute_loss(self, logits, labels, epoch):
    18          norms = torch.norm(logits, p=2, dim=1, keepdim=True)
    19          logits_norm = logits / (norms + 1e-7)
    20          scaled_logits = logits_norm / self.tau
    21          return F.cr