arXiv subjects

Zhechen Li

Publications and source records attributed to Zhechen Li.

6 recordsLinked to original sources

arXiv2026

Differentially Oblivious Priority Queues with Sublogarithmic Cost

In this paper, we study exact online integer priority queues under differential obliviousness in the balls-and-bins model. Given a public upper bound $T$ on the number of operations and assuming that the priorities are bounded by a polynomial in $T$, we construct an $(\eps,δ)$-differentially oblivious priority queue supporting insertion and minimum extraction, which requires $\polylog(T)$ private memory. For constant $\varepsilon>0$ and $δ=\exp(-Θ(\log^2 T))$, our construction incurs $O(\log T/\log\log T)$ expected amortized total work per operation, which breaks the $Ω(\log T)$ barrier for fully oblivious priority queues~[Jacob et al., SODA'2019]. Further, we prove a matching lower bound for $(\eps,δ)$-differentially oblivious priority queues with $\varepsilon=O(1)$ and $δ=o(1)$, which indicates that our construction is asymptotically optimal in terms of total work.

cs.DS↗

arXiv2026

O2N2: Efficient Doubly Oblivious Approximate Nearest Neighbor Search

Privacy-preserving approximate nearest neighbor (ANN) search allows clients to search from an untrusted server without revealing sensitive queries, and is useful in scenarios like medical diagnostics and credit scoring. Among the various privacy-preserving primitives, trusted execution environments (TEEs) have much lower overhead than heavy cryptographic ones, yet they leak memory access patterns, enabling adversaries to infer query contents. Oblivious RAM (ORAM) can be used to hide those access patterns at $\mathrm{O}(\log N)$ asymptotic cost, but existing high-performance ANN search algorithms (e.g., IVF or HNSW) exhibit complex, data-dependent access patterns that are unfriendly for ORAM. Furthermore, standard ORAM constructions typically assume there exists a small amount of trusted storage, which weakens the security guarantees and is not always feasible. Solutions that eliminate such trusted storage (known as {\em double obliviousness}) typically degrade performance further to $\mathrm{O}(\log^2 N)$. We introduce O2N2, a doubly oblivious ANN framework built around single-path search in randomly rotated $k$-d tree forests. A pointer-based Circuit ORAM stores the search structure, with separate storage for compact internal nodes and vector-bearing leaves. Each visited leaf is processed by an oblivious selection network, and fixed-schedule duplicate removal and selection combine the local results into the final top-$k$. We evaluate O2N2 on real-world datasets and further test its scalability on 100 million vectors. For 10-NN and 100-NN search at the specified high-recall targets, it achieves a $9.8$--$125.9\times$ speedup over the fastest doubly oblivious baselines.

cs.CR↗

arXiv2026

Probabilistic Mechanism Design in Diffusion Auctions

A diffusion auction refers to a selling process conducted over a social network, where each participant submits a bid and may invite other potential buyers to join the auction. Although various mechanisms have been proposed, none of them can simultaneously achieve incentive compatibility, non-negative revenue, and approximate efficiency with a constant approximation bound. In this paper, we propose the Probabilistic Diffusion Mechanism (PDM), a novel mechanism tailored for path graphs, which satisfies all three desired properties. We further extend PDM to general network structures through a map $f$, resulting in the $f$-PDM mechanism, which preserves the key properties of the original design. Beyond these, when $f$ satisfies properties such as breadth-first order, $f$-PDM also ensures Sybil-proofness and provides approximate revenue. Furthermore, to address buyer collusion, we introduce a modified version of the mechanism that balances collusion-proofness with revenue approximation. Finally, we extend the design to multi-unit diffusion auctions -- a more challenging setting -- and propose a simple yet effective mechanism, Multi-Unit PDM (MUPDM), that achieves approximate efficiency while maintaining IC. Moreover, we design Sybil-Proof MUPDM (SP-MUPDM) to resist Sybil attacks in the multi-item scenario.

cs.GT↗

arXiv2024

Differentially Private Approval-Based Committee Voting

In this paper, we investigate tradeoffs among differential privacy (DP) and several representative axioms for approval-based committee voting, including justified representation, proportional justified representation, extended justified representation, Pareto efficiency, and Condorcet criterion. Without surprise, we demonstrate that all of these axioms are incompatible with DP, and thus establish both upper and lower bounds for their two-way tradeoffs with DP. Furthermore, we provide upper and lower bounds for three-way tradeoffs among DP and every pairwise combination of such axioms, revealing that although these axioms are compatible without DP, their optimal levels under DP cannot be simultaneously achieved. Our results quantify the effect of DP on the satisfaction and compatibility of the axioms in approval-based committee voting, which can provide insights for designing voting rules that possess both privacy and axiomatic properties.

cs.GT↗

arXiv2023

Trading Off Voting Axioms for Privacy

In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet consistency, and Condorcet loser criterion. We provide upper and lower bounds on the two-way tradeoffs between DP and each axiom. We also provide upper and lower bounds on three-way tradeoffs among DP and every pairwise combination of all the axioms, showing that, while the axioms are compatible without DP, their upper bounds cannot be achieved simultaneously under DP. Our results illustrate the effect of DP on the satisfaction and compatibility of voting axioms.

cs.GT↗

arXiv2022

Differentially Private Condorcet Voting

Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy of randomized voting rules based on the well-known Condorcet method, and focus on three classes of voting rules in this family: Laplacian Condorcet method ($\CMLAP_λ$), exponential Condorcet method ($\CMEXP_λ$), and randomized response Condorcet method ($\CMRR_λ$), where $λ$ represents the level of noise. We prove that all of our rules satisfy absolute monotonicity, lexi-participation, probabilistic Pareto efficiency, approximate probabilistic Condorcet criterion, and approximate SD-strategyproofness. In addition, $\CMRR_λ$ satisfies (non-approximate) probabilistic Condorcet criterion, while $\CMLAP_λ$ and $\CMEXP_λ$ satisfy strong lexi-participation. Finally, we regard differential privacy as a voting axiom, and discuss its relations to other axioms.

cs.GT↗