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Heap pollution in Java can occur when type arguments and variables are not reified at run-time. As a result, different parameterized types are implemented by the same class or interface at run time. All invocations of a given generic type declaration share a single run-time implementation. This results in the possibility of heap pollution. [2]
A heap overflow, heap overrun, or heap smashing is a type of buffer overflow that occurs in the heap data area. Heap overflows are exploitable in a different manner to that of stack-based overflows. Memory on the heap is dynamically allocated at runtime and typically contains program data.
The heapsort algorithm can be divided into two phases: heap construction, and heap extraction. The heap is an implicit data structure which takes no space beyond the array of objects to be sorted; the array is interpreted as a complete binary tree where each array element is a node and each node's parent and child links are defined by simple arithmetic on the array indexes.
To insert an element to a heap, we perform the following steps: Add the element to the bottom level of the heap at the leftmost open space. Compare the added element with its parent; if they are in the correct order, stop.
Buffer overflow – out-of-bound writes can corrupt the content of adjacent objects, or internal data (like bookkeeping information for the heap) or return addresses. Buffer over-read – out-of-bound reads can reveal sensitive data or help attackers bypass address space layout randomization. Temporal
Executable space protection is an approach to buffer overflow protection that prevents execution of code on the stack or the heap. An attacker may use buffer overflows to insert arbitrary code into the memory of a program, but with executable space protection, any attempt to execute that code will cause an exception.
In computer science, a strict Fibonacci heap is a priority queue data structure with low worst case time bounds. It matches the amortized time bounds of the Fibonacci heap in the worst case. To achieve these time bounds, strict Fibonacci heaps maintain several invariants by performing restoring transformations after every operation.
Illustration of the table-heap compaction algorithm. Objects that the marking phase has determined to be reachable (live) are colored, free space is blank. A table-based algorithm was first described by Haddon and Waite in 1967. [1] It preserves the relative placement of the live objects in the heap, and requires only a constant amount of overhead.