A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets
Abhijeet Ghoshal, Yan Li, Syam Menon, Sumit Sarkar
TL;DR
This paper addresses hiding sensitive itemsets in transaction data by proposing a quantum‑classical heuristic based on the Quantum Approximate Optimization Algorithm (QAOA). To fit QAOA, the authors dualize the frequent itemset hiding constraints and map the problem to an Ising Hamiltonian, then solve the resulting binary quadratic optimization with QAOA and a classical optimizer. Experiments on small datasets using IBM Qiskit show the quantum‑inspired heuristic delivers near‑optimal solutions, demonstrating a viable pathway for applying near‑term quantum devices to privacy‑preserving data mining. The work highlights the potential of quantum–classical hybrids for real‑world data privacy problems and outlines concrete directions for scaling and hardware‑level validation in future research.
Abstract
Quantum devices use qubits to represent information, which allows them to exploit important properties from quantum physics, specifically superposition and entanglement. As a result, quantum computers have the potential to outperform the most advanced classical computers. In recent years, quantum algorithms have shown hints of this promise, and many algorithms have been proposed for the quantum domain. There are two key hurdles to solving difficult real-world problems on quantum computers. The first is on the hardware front -- the number of qubits in the most advanced quantum systems is too small to make the solution of large problems practical. The second involves the algorithms themselves -- as quantum computers use qubits, the algorithms that work there are fundamentally different from those that work on traditional computers. As a result of these constraints, research has focused on developing approaches to solve small versions of problems as proofs of concept -- recognizing that it would be possible to scale these up once quantum devices with enough qubits become available. Our objective in this paper is along the same lines. We present a quantum approach to solve a well-studied problem in the context of data sharing. This heuristic uses the well-known Quantum Approximate Optimization Algorithm (QAOA). We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms. The results show that the method has potential and provide answers close to optimal. At the same time, we realize there are opportunities for improving the method further.