The computational hardness of the Elliptic Curve Discrete Logarithm Problem (ECDLP) directly determines Elliptic-Curve Cryptography (ECC) security. Quantum Annealing (QA) leverages its unique tunneling effects to effectively escape local optima. This paper proposes a quantum-annealing-based computational framework for attacking ECDLP by integrating QA with the index calculus method, achieving a quantum-solving attack on ECDLP. First, a relation-generation method based on Semaev summation polynomials is used to map the complex elliptic-curve point-addition constraints onto an Ising Hamiltonian. Next, by restructuring the penalty-term formulation and applying dynamic coefficient optimization, the parameter scale of the Ising Hamiltonian is compressed by 80%, thereby improving the QA success probability. Finally, the ECDLP is successfully modeled and solved on the D-Wave Advantage2 quantum computer. Experimental results demonstrate that the proposed quantum computing framework solves a 16-bit prime-field elliptic curve ECDLP on real quantum hardware, representing the highest experimental record publicly reported to date internationally, and further validating the practical attack potential and scalability of QA in cryptanalytic scenarios.

Traditional k-anonymity schemes cannot protect a user’s privacy perfectly in big data and mobile network environments. In fact, existing k-anonymity schemes only protect location in datasets with small granularity. But in larger granularity datasets, a user’s geographical region-location is always exposed in realizations of k-anonymity because of interaction with neighboring nodes. And if a user could not find enough adjacent access points, most existing schemes would be invalid. How to protect location information has become an important issue. But it has not attracted much attention. To solve this problem, two location-privacy protection models are proposed. Then a new generalized k-anonymity Location Privacy Protection Scheme based on the Chinese Remainder Theorem (LPSS-CRT) in Location-Based Services (LBSs) is proposed. We prove that it can guarantee that users can access LBSs without leaking their region-location information, which means the scheme can achieve perfect anonymity. Analysis shows that LPPS-CRT is more secure in protecting location privacy, including region information, and is more efficient, than similar schemes. It is suitable for dynamic environments for different users’ privacy protection requests.