We consider a new group testing model wherein each item is a binary random variable defined by an a priori probability of being defective. We assume that each probability is small and that items are independent, but not necessarily identically distributed. The goal of group testing algorithms is to identify with high probability the subset of defectives via non-linear (disjunctive) binary measurements. Our main contributions are two classes of algorithms: (1) adaptive algorithms with tests based either on a maximum entropy principle, or on a ShannonFano/Huffman code; (2) non-adaptive algorithms. Under loose assumptions and with high probability, our algorithms only need a number of measurements that is close to the informationtheoretic lower bound, up to an explicitly-calculated universal constant factor.
Abstract-Virtual function calls are one of the most popular control-flow hijack attack targets. Compilers use a virtual function pointer table, called a VTable, to dynamically dispatch virtual function calls. These VTables are read-only, but pointers to them are not. VTable pointers reside in objects that are writable, allowing attackers to overwrite them. As a result, attackers can divert the control-flow of virtual function calls and launch VTable hijacking attacks. Researchers have proposed several solutions to protect virtual calls. However, they either incur high performance overhead or fail to defeat some VTable hijacking attacks.In this paper, we propose a lightweight defense solution, VTrust, to protect all virtual function calls from VTable hijacking attacks. It consists of two independent layers of defenses: virtual function type enforcement and VTable pointer sanitization. Combined with modern compilers' default configuration, i.e., placing VTables in read-only memory, VTrust can defeat all VTable hijacking attacks and supports modularity, allowing us to harden applications module by module. We have implemented a prototype on the LLVM compiler framework. Our experiments show that this solution only introduces a low performance overhead, and it defeats real world VTable hijacking attacks.
We introduce and study a general version of the fractional online knapsack problem with multiple knapsacks, heterogeneous constraints on which items can be assigned to which knapsack, and rate-limiting constraints on the assignment of items to knapsacks. This problem generalizes variations of the knapsack problem and of the one-way trading problem that have previously been treated separately, and additionally finds application to the real-time control of electric vehicle (EV) charging. We introduce a new algorithm that achieves a competitive ratio within an additive factor of one of the best achievable competitive ratios for the general problem and matches or improves upon the best-known competitive ratio for special cases in the knapsack and one-way trading literatures. Moreover, our analysis provides a novel approach to online algorithm design based on an instance-dependent primal-dual analysis that connects the identification of worst-case instances to the design of algorithms. Finally, we illustrate the proposed algorithm via trace-based experiments of EV charging.
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