不相信任何人:使用隐私保护计算阻止“心脏出血”攻击

N. G. Tsoutsos, M. Maniatakos
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引用次数: 7

摘要

OpenSSL库中的一个安全漏洞(代号为Heartbleed)允许攻击者读取相应服务器内存的内容,从而有效地泄露密码、主密钥和用户的会话cookie。只要服务器内存内容是清晰的,下一次错误/攻击将信息交给攻击者只是时间问题。在本文中,我们研究了保护隐私的通用计算的适用性,这种计算可能会使任何泄露的信息无法被攻击者破译。通过使用同态加密的内存内容来确保隐私。为此,我们探索了用户数据隐私约束下通用计算的边界。具体来说,我们探讨了通用计算所需的最小信息量,这通常需要控制流和分支,以及这些信息在多大程度上可以不受理论上具有无限资源的威胁的影响,包括对目标系统内部的访问。
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Trust No One: Thwarting "heartbleed" Attacks Using Privacy-Preserving Computation
A security bug in the OpenSSL library, codenamed Heartbleed, allowed attackers to read the contents of the corresponding server's memory, effectively revealing passwords, master keys, and users' session cookies. As long as the server memory contents are in the clear, it is a matter of time until the next bug/attack hands information over to attackers. In this paper, we investigate the applicability of privacy-preserving general-purpose computation, that would potentially render any information leaked indecipherable to attackers. Privacy is ensured by the use of homomorphically-encrypted memory contents. To this end, we explore the boundaries of general-purpose computation constrained for user data privacy. Specifically, we explore the minimum amount of information required for general purpose computation, which typically requires control flow and branches, and to what extent such information can be kept private from threats that have theoretically unlimited resources, including access to the internals of a target system.
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