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    • 4. 发明授权
    • PROTECTING AN ITEM OF SOFTWARE
    • 保护软件项目
    • EP3127037B1
    • 2018-05-09
    • EP15714189.6
    • 2015-03-30
    • Irdeto B.V.
    • GU, Yuan XiangJOHNSON, Harold
    • G06F21/55
    • G06F21/556G06F2221/033
    • There is described a method of protecting an item of software so as to obfuscate a condition which causes a variation in control flow through a portion of the item of software dependent on whether the condition is satisfied, wherein satisfaction of the condition is based on evaluation of one or more condition variables. The method comprises: (i) modifying the item of software such that the control flow through said portion is not dependent on whether the condition is satisfied; and (ii) inserting a plurality of identity transformations into expressions in said portion of the modified item of software, wherein the identity transformations are defined and inserted such that, in the absence of tampering, they maintain the results of the expressions if the condition is satisfied and such that they alter the results of the expressions if the condition is not satisfied, wherein each identity transformation is directly or indirectly dependent on at least one of the one or more condition variables. New variables may be defined as part of this method. There are also described associated apparatuses, computer programs and the like.
    • 8. 发明公开
    • METHODS AND SYSTEMS FOR THWARTING SIDE CHANNEL ATTACKS
    • 用于消除边信道攻击的方法和系统
    • EP3161711A1
    • 2017-05-03
    • EP15731800.7
    • 2015-06-16
    • Qualcomm Incorporated
    • PATNE, Satyajit PrabhakarGUPTA, RajarshiXIAO, Lu
    • G06F21/55
    • H04L63/1416G06F21/556G06F2221/2125H04L63/1433
    • A computing device may use machine learning techniques to determine the level, degree, and severity of its vulnerability to side channel attacks. The computing device may intelligently and selectively perform obfuscation operations (e.g., operations to raise the noise floor) to prevent side channel attacks based on the determined level, degree, or severity of its current vulnerability to such attacks. The computing device may also monitor the current level of natural obfuscation produced by the device, determining whether there is sufficient natural obfuscation to prevent a side channel attack during an ongoing critical activity, and perform the obfuscation operation during the ongoing critical activity and in response to determining that there is not sufficient natural obfuscation to adequately protect the computing device against side channel attacks.
    • 计算设备可以使用机器学习技术来确定其对边信道攻击的易受攻击的程度,程度和严重程度。 计算设备可以智能地并且选择性地执行混淆操作(例如,用于提高本底噪声的操作)以基于其当前对这种攻击的脆弱性的所确定的级别,程度或严重程度来防止旁道攻击。 计算设备还可以监视由设备产生的自然迷惑的当前级别,确定是否有足够的自然迷惑来防止正在进行的关键活动期间的旁道攻击,并且在正在进行的关键活动期间执行迷惑操作,并且响应于 确定没有足够的自然混淆来充分保护计算设备免受旁道攻击。