差分隐私下用户购买记录频数发布方案实现

"""import random\nfrom collections import Counter\n\n# 读取数据集\ndef read_dataset(filename):\n dataset = []\n with open(filename, 'r') as file:\n for line in file:\n transaction = line.strip().split(' ')\n dataset.append(transaction)\n return dataset\n\n# 获取所有的item\ndef get_all_items(dataset):\n items = set()\n for transaction in dataset:\n items.update(transaction)\n return items\n\n# 发布频数分布信息\ndef publish_frequency(dataset):\n items = get_all_items(dataset)\n frequency = Counter()\n for transaction in dataset:\n frequency.update(transaction)\n result = [frequency[item] for item in items]\n return result\n\n# 差分隐私下使用拉普拉斯机制进行扰动\ndef laplace_mechanism(count, epsilon):\n sensitivity = 1\n scale = sensitivity / epsilon\n noise = random.laplace(0, scale)\n return count + noise\n\n# 本地化差分隐私下使用randomized response机制进行扰动\ndef randomized_response(count, epsilon):\n prob = 1 - (1 / (1 + epsilon))\n if random.random() < prob:\n return count\n else:\n return random.randint(0, count) # 扰动为随机值\n\n# 评估指标：计算MSE\ndef calculate_mse(original, noisy):\n mse = sum((x - y) ** 2 for x, y in zip(original, noisy)) / len(original)\n return mse\n\n# 敏感度分析\ndef sensitivity_analysis(dataset):\n max_length = max(len(transaction) for transaction in dataset)\n return max_length\n\n# 主函数\ndef main():\n dataset = read_dataset('dataset.txt')\n sensitivity = sensitivity_analysis(dataset)\n\n # 发布频数分布信息\n original_result = publish_frequency(dataset)\n\n # 差分隐私下的发布方案\n epsilon_list = [0.1, 1, 2, 4]\n laplace_mse = []\n for epsilon in epsilon_list:\n noisy_result = [laplace_mechanism(count, epsilon) for count in original_result]\n mse = calculate_mse(original_result, noisy_result)\n laplace_mse.append(mse)\n\n # 本地化差分隐私下的发布方案\n randomized_response_mse = []\n for epsilon in epsilon_list:\n noisy_result = [randomized_response(count, epsilon) for count in original_result]\n mse = calculate_mse(original_result, noisy_result)\n randomized_response_mse.append(mse)\n\n # 输出结果\n print("Sensitivity:", sensitivity)\n print("Original Result:", original_result)\n print("Laplace Mechanism MSE:", laplace_mse)\n print("Randomized Response MSE:", randomized_response_mse)\n\nif name == 'main':\n main()\n"""\n\n请将数据集保存为dataset.txt，每行表示一个用户的购买记录。\n\n此代码实现了以下功能：\n1. 读取数据集并获取所有的item。\n2. 发布频数分布信息，即每个item被购买的次数。\n3. 使用拉普拉斯机制和randomized response机制对频数分布信息进行扰动，实现差分隐私和本地化差分隐私下的发布方案。\n4. 计算并比较两种隐私方案在不同隐私预算下的MSE。\n\n请根据实际数据集和需求进行适当的修改和扩展。