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A partially hidden policy CP-ABE scheme against attribute values guessing attacks with online privacy-protective decryption testing in IoT assisted cloud computing

机译:针对IoT辅助云计算中的在线隐私保护解密测试的猜测攻击的属性值的部分隐藏的政策CP-ABE计划

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摘要

In recent years, to address the security defect that the explicit attribute values in access policies may reveal the privacy, a new variant of ciphertext-policy attribute-based encryption(CP-ABE)--hidden policy CP-ABE (HP-CP-ABE) is proposed in some recent works. But there are two tremendous flaws in most existing HP-CP-ABE schemes. The one issue is that an attacker can launch the attribute values guessing attacks (AVGA) to detect the attribute values in access policies of many HP-CP-ABE schemes. And another issue is that, if the HP-CP-ABE schemes are using the "Linear Secret Sharing Schemes (LSSS)" as their access structures, as the rows of the LSSS matrix grows, the time complexity of the decryption testing algorithm will boost rapidly which will greatly aggravate the computing burden of the user. So in this paper, we propose a partially HP-CP-ABE (PHP-CP-ABE) scheme which can perfectly withstand the attribute values guessing attacks (AVGA). As our access structure is using the LSSS, to alleviate the computing burden of the user, we design a online privacy-protective decryption testing algorithm for the users to privately and securely outsource the decryption testing phase to the cloud server. Our online testing algorithm is privacy-protective which means during running the privacy-protective decryption testing algorithm, the cloud server has no chance to know anything about the attribute values in the access policy and the attribute values of the user. This will prevent the privacy from leaking out to the third party cloud server. Then we rigorously prove that our scheme is selectively indistinguishable secure under chosen plaintext attacks (IND-CPA). Next, by reduction to the computational q-PBDHE assumption which is firstly proposed in our paper, we prove that our HP-CP-ABE scheme is indistinguishable secure under the attribute values guessing attacks (IND-AVGA). Finally through the comparison with the state-of-art HP-CP-ABE schemes from the perspective of functionality and efficiency, it is easily to observe that our scheme has high-security and high-efficiency. In appendix, we give a straightaway analysis to some relevant works to point out the security vulnerabilities in their schemes.
机译:近年来,要解决访问策略中的显式属性值可能会揭示隐私,这是基于密文 - 策略属性的加密(CP-ABE) - 隐藏策略CP-ABE(HP-CP - ABE)在最近的一些作品中提出。但在大多数现有的HP-CP-ABE方案中有两个巨大的缺陷。一个问题是攻击者可以启动猜测攻击(AVGA)的属性值来检测许多HP-CP-ABE方案的访问策略中的属性值。另一个问题是,如果HP-CP-ABE方案使用“线性秘密共享方案(LSSS)”作为其访问结构,因为LSSS矩阵的行增加,解密测试算法的时间复杂度将提升迅速将大大加剧用户的计算负担。因此,在本文中,我们提出了部分HP-CP-ABE(PHP-CP-ABE)方案,可以完全抵消猜测攻击(AVGA)的属性值。随着我们的访问结构正在使用LSSS,为了减轻用户的计算负担,我们设计了一个用于用户的在线隐私保护解密测试算法,以私下并将解密测试阶段秘密地将解密测试阶段外包给Cloud Server。我们的在线测试算法是隐私保护,这意味着在运行隐私保护解密测试算法期间,云服务器没有机会在访问策略中的属性值和用户的属性值中了解任何内容。这将阻止隐私泄漏到第三方云服务器。然后,我们严格证明我们的方案在选择的明文攻击(IND-CPA)下选择性地无法区分安全。接下来,通过减少到首先在本文中提出的计算Q-PBDHE假设,我们证明我们的HP-CP-ABE方案在猜测攻击(Ind-Avga)的属性值下是难以区分的安全。最后通过与功能和效率的角度来看通过与最先进的HP-CP-CP-ABE方案进行比较,很容易观察我们的计划具有高安全性和高效率。在附录中,我们向某些相关工程提供直接分析,以指出其计划中的安全漏洞。

著录项

  • 来源
    《Future generation computer systems》 |2021年第10期|181-195|共15页
  • 作者单位

    Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China;

    Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China;

    Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Hidden policy CP-ABE (HP-CP-ABE); Against attribute values guessing attacks; (AVGA); Online privacy-protective decryption; testing algorithm;

    机译:隐藏的政策CP-ABE(HP-CP-ABE);反对猜测攻击的属性值;(AVGA);在线隐私保护性解密;测试算法;
  • 入库时间 2022-08-19 02:30:24

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