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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  "op": "replace",
3  "filename": "opacus/bench/membership/custom_membership_defense.py",
4  "start_line": 1,
5  "end_line": 23,
6  "content": "\"\"\"Editable membership-inference defense for MLS-Bench.\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\n\n# ============================================================\n# EDITABLE\n# ============================================================\nclass MembershipDefense:\n    \"\"\"Training-time regularizer for privacy-utility tradeoffs.\"\"\"\n\n    def __init__(self):\n        self.label_smoothing = 0.1\n        self.entropy_weight = 0.05\n        self.mixup_alpha = 0.2  # Beta distribution parameter for mixup\n        \n    def compute_loss(self, logits, labels, model, epoch):\n        # Mixup regularization\n        batch_size = logits.size(0)\n        indices = torch.randperm(batch_size, device=logits.device)\n        lam = torch.distributions.beta.Beta(self.mixup_alpha, self.mixup_alpha).sample((batch_size,)).to(logits.device)\n        # Ensure lam is in [0,1] and shape (batch_size,1) for broadcasting\n        lam = lam.view(-1, 1)\n        mixed_logits = lam * logits + (1 - lam) * logits[indices]\n        \n        # Convert labels to one-hot\n        num_classes = logits.size(1)\n        labels_onehot = F.one_hot(labels, num_classes).float()\n        labels_mixed = lam * labels_onehot + (1 - lam) * labels_onehot[indices]\n        \n        # Cross-entropy with label smoothing on mixed targets\n        ce = F.cross_entropy(mixed_logits, labels_mixed, label_smoothing=self.label_smoothing)\n        \n        # Entropy bonus on original logits (optional)\n        probs = torch.softmax(logits, dim=1)\n        entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=1).mean()\n        \n        total_loss = ce - self.entropy_weight * entropy\n        return total_loss\n# ============================================================\n# END EDITABLE\n# ============================================================"
7}

Tool ResultStep 1

OK: Replaced lines 1..23 with 43 line(s) in opacus/bench/membership/custom_membership_defense.py. Editable range: entire file.

[Current file: opacus/bench/membership/custom_membership_defense.py | editable: 1–43 | total: 43 lines]
     1  """Editable membership-inference defense for MLS-Bench."""
     2  
     3  import torch
     4  import torch.nn.functional as F
     5  import numpy as np
     6  
     7  # ============================================================
     8  # EDITABLE
     9  # ============================================================
    10  class MembershipDefense:
    11      """Training-time regularizer for privacy-utility tradeoffs."""
    12  
    13      def __init__(self):
    14          self.label_smoothing = 0.1
    15          self.entropy_weight = 0.05
    16          self.mixup_alpha = 0.2  # Beta distribution parameter for mixup
    17          
    18      def compute_loss(self, logits, labels, model, epoch):
    19          # Mixup regularizati

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=2.1891
TRAIN_METRICS epoch=2 loss=2.0105
TRAIN_METRICS epoch=3 loss=1.6003
TRAIN_METRICS epoch=4 loss=1.2672
TRAIN_METRICS epoch=5 loss=1.0963
mnist: test_acc=0.8390, mia_auc=0.5609, privacy_gap=0.0423, privacy_score=0.7781

### fmnist (scripts/fmnist.sh)
TRAIN_METRICS epoch=1 loss=2.1630
TRAIN_METRICS epoch=2 loss=1.6743
TRAIN_METRICS epoch=3 loss=1.3761
TRAIN_METRICS epoch=4 loss=1.2429
TRAIN_METRICS epoch=5 loss=1.1756
fmnist: test_acc=0.7395, mia_auc=0.5212, privacy_gap=0.0157, privacy_score=0.7183

### cifar10 (scripts/cifar10.sh)
TRAIN_METRICS epoch=1 loss=2.1637
TRAIN_METRICS epoch=2 loss=2.0387
TRAIN_METRICS epoch=3 loss=1.9829
TRAIN_METRICS epoch=4 loss=1.9392
TRAIN_METRICS epoch=5 loss=1.9037
TRAIN_METRICS epoch=6 loss=1.9035
cifar10: test_acc=0.3230, mia_auc=0.5017, privacy_gap=0.0012, privacy_score=0.3213


## Seed 123

### mnist (scrip

AssistantStep 3undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 3

Undo complete:
Restored: opacus/bench/membership/custom_membership_defense.py

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "filename": "opacus/bench/membership/custom_membership_defense.py",
4  "start_line": 1,
5  "end_line": 23,
6  "content": "\"\"\"Editable membership-inference defense for MLS-Bench.\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n# ============================================================\n# EDITABLE\n# ============================================================\nclass MembershipDefense:\n    \"\"\"Training-time regularizer for privacy-utility tradeoffs.\"\"\"\n\n    def __init__(self):\n        self.label_smoothing = 0.1\n        self.focal_gamma = 2.0\n        self.entropy_weight = 0.02\n        \n    def compute_loss(self, logits, labels, model, epoch):\n        # Cross-entropy with label smoothing\n        ce_per = F.cross_entropy(logits, labels, reduction='none', label_smoothing=self.label_smoothing)\n        \n        # Focal weight based on confidence\n        probs = torch.softmax(logits, dim=1)\n        pt = probs.gather(1, labels.unsqueeze(1)).squeeze(1)  # probability of true class\n        focal_weight = (1 - pt) ** self.focal_gamma\n        focal_loss = focal_weight * ce_per\n        \n        # Entropy bonus\n        entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=1)\n        \n        total_per = focal_loss - self.entropy_weight * entropy\n        return total_per.mean()\n# ============================================================\n# END EDITABLE\n# ============================================================"
7}

Tool ResultStep 4

OK: Replaced lines 1..23 with 34 line(s) in opacus/bench/membership/custom_membership_defense.py. Editable range: entire file.

[Current file: opacus/bench/membership/custom_membership_defense.py | editable: 1–34 | total: 34 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.label_smoothing = 0.1
    14          self.focal_gamma = 2.0
    15          self.entropy_weight = 0.02
    16          
    17      def compute_loss(self, logits, labels, model, epoch):
    18          # Cross-entropy with label smoothing
    19          ce_per = F.cross_entropy(logits, la

AssistantStep 5test

Tool ResultStep 5

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

### mnist (scripts/mnist.sh)
TRAIN_METRICS epoch=1 loss=1.8034
TRAIN_METRICS epoch=2 loss=1.2453
TRAIN_METRICS epoch=3 loss=0.4717
TRAIN_METRICS epoch=4 loss=0.2282
TRAIN_METRICS epoch=5 loss=0.1519
mnist: test_acc=0.9020, mia_auc=0.5439, privacy_gap=0.0233, privacy_score=0.8581

### fmnist (scripts/fmnist.sh)
TRAIN_METRICS epoch=1 loss=1.5950
TRAIN_METRICS epoch=2 loss=0.8192
TRAIN_METRICS epoch=3 loss=0.6163
TRAIN_METRICS epoch=4 loss=0.5087
TRAIN_METRICS epoch=5 loss=0.4597
fmnist: test_acc=0.7050, mia_auc=0.5231, privacy_gap=0.0159, privacy_score=0.6819

### cifar10 (scripts/cifar10.sh)
TRAIN_METRICS epoch=1 loss=1.7494
TRAIN_METRICS epoch=2 loss=1.5514
TRAIN_METRICS epoch=3 loss=1.4054
TRAIN_METRICS epoch=4 loss=1.3046
TRAIN_METRICS epoch=5 loss=1.2844
TRAIN_METRICS epoch=6 loss=1.2050
cifar10: test_acc=0.3670, mia_auc=0.5030, privacy_gap=0.0020, privacy_score=0.3640

[Leaderboard] Results saved: {'test_acc_mnis

AssistantStep 6edit

Tool Input

1{
2  "op": "replace",
3  "filename": "opacus/bench/membership/custom_membership_defense.py",
4  "start_line": 1,
5  "end_line": 34,
6  "content": "\"\"\"Editable membership-inference defense for MLS-Bench.\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n# ============================================================\n# EDITABLE\n# ============================================================\nclass MembershipDefense:\n    \"\"\"Training-time regularizer for privacy-utility tradeoffs.\"\"\"\n\n    def __init__(self):\n        self.label_smoothing = 0.1\n        self.entropy_weight = 0.05\n        self.confidence_threshold = 0.9\n        self.penalty_weight = 0.1\n        \n    def compute_loss(self, logits, labels, model, epoch):\n        # Cross-entropy with label smoothing\n        ce = F.cross_entropy(logits, labels, label_smoothing=self.label_smoothing)\n        \n        probs = torch.softmax(logits, dim=1)\n        \n        # Entropy bonus\n        entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=1).mean()\n        \n        # Confidence penalty: penalize probabilities above threshold\n        max_prob = probs.max(dim=1)[0]\n        excess = torch.relu(max_prob - self.confidence_threshold)\n        penalty = (excess ** 2).mean()\n        \n        total_loss = ce - self.entropy_weight * entropy + self.penalty_weight * penalty\n        return total_loss\n# ============================================================\n# END EDITABLE\n# ============================================================"
7}

Tool ResultStep 6

OK: Replaced lines 1..34 with 36 line(s) in opacus/bench/membership/custom_membership_defense.py. Editable range: entire file.

[Current file: opacus/bench/membership/custom_membership_defense.py | editable: 1–36 | 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      """Training-time regularizer for privacy-utility tradeoffs."""
    11  
    12      def __init__(self):
    13          self.label_smoothing = 0.1
    14          self.entropy_weight = 0.05
    15          self.confidence_threshold = 0.9
    16          self.penalty_weight = 0.1
    17          
    18      def compute_loss(self, logits, labels, model, epoch):
    19          # Cross-entropy with label smoothing

AssistantStep 7test

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[Test #3] (0 tests remaining)


## Seed 42

### mnist (scripts/mnist.sh)
TRAIN_METRICS epoch=1 loss=2.1775
TRAIN_METRICS epoch=2 loss=1.8509
TRAIN_METRICS epoch=3 loss=1.2572
TRAIN_METRICS epoch=4 loss=0.8743
TRAIN_METRICS epoch=5 loss=0.7663
mnist: test_acc=0.9155, mia_auc=0.5594, privacy_gap=0.0342, privacy_score=0.8561

### fmnist (scripts/fmnist.sh)
TRAIN_METRICS epoch=1 loss=2.0660
TRAIN_METRICS epoch=2 loss=1.5047
TRAIN_METRICS epoch=3 loss=1.2723
TRAIN_METRICS epoch=4 loss=1.1560
TRAIN_METRICS epoch=5 loss=1.0791
fmnist: test_acc=0.7120, mia_auc=0.5210, privacy_gap=0.0162, privacy_score=0.6910

### cifar10 (scripts/cifar10.sh)
TRAIN_METRICS epoch=1 loss=2.1317
TRAIN_METRICS epoch=2 loss=1.9851
TRAIN_METRICS epoch=3 loss=1.8916
TRAIN_METRICS epoch=4 loss=1.7919
TRAIN_METRICS epoch=5 loss=1.7174
TRAIN_METRICS epoch=6 loss=1.6810
cifar10: test_acc=0.4360, mia_auc=0.4990, privacy_gap=-0.0001, privacy_score=0.4360


## Seed 123

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

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_membership_defense.py",
4  "start_line": 1,
5  "end_line": 32,
6  "content": "\"\"\"Editable membership-inference defense for MLS-Bench.\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n# ============================================================\n# EDITABLE\n# ============================================================\nclass MembershipDefense:\n    \"\"\"Margin-aware entropy regularization for membership privacy.\n\n    Loss = CrossEntropy - α * entropy(p) + β * max(0, margin - margin_target)\n    where margin = p_max - p_second_max.\n    Encourages predictions to be both uncertain (high entropy) and have\n    small gap between top two probabilities, reducing overconfidence.\n    \"\"\"\n\n    def __init__(self):\n        self.entropy_weight = 0.1   # α\n        self.margin_weight = 0.05   # β\n        self.margin_target = 0.3    # target margin to penalize above\n\n    def compute_loss(self, logits, labels, epoch):\n        # Cross-entropy\n        ce = F.cross_entropy(logits, labels)\n        \n        # Predictive entropy bonus\n        probs = torch.softmax(logits, dim=1)\n        entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=1).mean()\n        \n        # Margin penalty\n        sorted_probs, _ = torch.sort(probs, dim=1, descending=True)\n        p_max = sorted_probs[:, 0]\n        p_second = sorted_probs[:, 1]\n        margin = p_max - p_second\n        margin_penalty = torch.clamp_min(margin - self.margin_target, 0).mean()\n        \n        total_loss = ce - self.entropy_weight * entropy + self.margin_weight * margin_penalty\n        return total_loss\n# ============================================================\n# END EDITABLE\n# ============================================================"
7}

Tool ResultStep 1

OK: Replaced lines 1..32 with 42 line(s) in pytorch-vision/custom_membership_defense.py. Editable range: entire file.

[Current file: pytorch-vision/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      """Margin-aware entropy regularization for membership privacy.
    11  
    12      Loss = CrossEntropy - α * entropy(p) + β * max(0, margin - margin_target)
    13      where margin = p_max - p_second_max.
    14      Encourages predictions to be both uncertain (high entropy) and have
    15      small gap between top two probabilities, reducing overconfidence.
    16      """
    17  
    18      def __init__(self):
    19

AssistantStep 2test

Tool ResultStep 2

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


## Seed 42

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
TRAIN_METRICS epoch=1 loss=1.6224 train_acc=32.90 lr=0.099975
TRAIN_METRICS epoch=10 loss=0.6084 train_acc=77.21 lr=0.097553
TRAIN_METRICS epoch=20 loss=0.4951 train_acc=81.59 lr=0.090451
TRAIN_METRICS epoch=30 loss=0.4208 train_acc=84.60 lr=0.079389
TRAIN_METRICS epoch=40 loss=0.3632 train_acc=86.81 lr=0.065451
TRAIN_METRICS epoch=50 loss=0.3173 train_acc=88.58 lr=0.050000
TRAIN_METRICS epoch=60 loss=0.2553 train_acc=91.14 lr=0.034549
TRAIN_METRICS epoch=70 loss=0.1845 train_acc=93.93 lr=0.020611
TRAIN_METRICS epoch=80 loss=0.1020 train_acc=97.54 lr=0.009549
TRAIN_METRICS epoch=90 loss=0.0624 train_acc=99.16 lr=0.002447
TRAIN_METRICS epoch=100 loss=0.0551 train_acc=99.53 lr=0.000000
resnet20-cifar10: test_acc=0.8816, mia_auc=0.5673, privacy_gap=0.0526, privacy_score=0.8143

### vgg16bn-cifar100 (scripts/vgg16bn_cifar100.sh)
TRAIN_METRICS epoch=1 loss=4

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_membership_defense.py",
4  "start_line": 1,
5  "end_line": 42,
6  "content": "\"\"\"Editable membership-inference defense for MLS-Bench.\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n# ============================================================\n# EDITABLE\n# ============================================================\nclass MembershipDefense:\n    \"\"\"Privacy-Calibrated Cross-Entropy with adaptive temperature.\n\n    Loss = CE(logits / T(epoch)) - α * entropy(p) + β * max(0, margin - margin_target)\n    where T(epoch) linearly increases from T_start to T_end over training.\n    Higher temperature smooths probabilities, reducing overconfidence.\n    \"\"\"\n\n    def __init__(self):\n        # Temperature annealing\n        self.temp_start = 1.0\n        self.temp_end = 2.0\n        # Regularization weights\n        self.entropy_weight = 0.1   # α\n        self.margin_weight = 0.05   # β\n        self.margin_target = 0.3    # target margin to penalize above\n        # Assume 100 epochs total (as in training loop)\n        self.total_epochs = 100\n\n    def compute_loss(self, logits, labels, epoch):\n        # Temperature annealing linear over epochs\n        temperature = self.temp_start + (self.temp_end - self.temp_start) * (epoch / (self.total_epochs - 1))\n        scaled_logits = logits / temperature\n        \n        # Temperature-scaled cross-entropy\n        ce = F.cross_entropy(scaled_logits, labels)\n        \n        # Predictive entropy bonus (on original logits or scaled?)\n        # Using scaled logits for consistency\n        probs = torch.softmax(scaled_logits, dim=1)\n        entropy = -(probs * torch.log(probs.clamp_min(1e-8))).sum(dim=1).mean()\n        \n        # Margin penalty on original probabilities (or scaled?)\n        # Use scaled probabilities as they are used for loss\n        sorted_probs, _ = torch.sort(probs, dim=1, descending=True)\n        p_max = sorted_probs[:, 0]\n        p_second = sorted_probs[:, 1]\n        margin = p_max - p_second\n        margin_penalty = torch.clamp_min(margin - self.margin_target, 0).mean()\n        \n        total_loss = ce - self.entropy_weight * entropy + self.margin_weight * margin_penalty\n        return total_loss\n# ============================================================\n# END EDITABLE\n# ============================================================"
7}