Tian Shu | Searcharxiv

arXiv2026

Function Privatization in the Local Model

We study the problem of privately releasing functions, with a particular focus on curves, which are images of continuous functions on some finite interval. Many types of data exist naturally as curves, such as trajectory data or $1$D density curves. We shall primarily be interested in the local model setting, where the function to be privatized captures data belonging to one individual, which is the more challenging setting with limited prior work. Under the standard notion of local differential privacy (DP), any two arbitrarily different functions are required to be made indistinguishable by privatization, which is too strong to allow meaningful utility; we thus work with a generalized notion of DP known as Geo-Privacy (GP), which allows functions far apart to be distinguished more easily while providing strong protection for near functions. To demonstrate the effectiveness of our framework, we provide experimental evaluation on several datasets.

cs.CR↗

arXiv2026

Parameterized Algorithms and Complexity for Function Merging with Branch Reordering

Binary size reduction is an increasingly important optimization objective for compilers. One emerging technique is function merging, where multiple similar functions are merged into one, thereby eliminating redundancy. The SOTA approach to perform the merging is based on sequence alignment, where functions are viewed as linear sequences of instructions that are then matched in a way maximizing their alignment. In this paper, we consider a significantly generalized formulation of the problem by allowing reordering of branches within each function, subsequently allowing for more flexible matching and better merging. We show that this makes the problem NP-hard, and thus we study it through the lens of parameterized algorithms and complexity, where we identify certain parameters of the input that govern its complexity. We look at two natural parameters: the branching factor and nesting depth of input functions. Concretely, our input consists of two functions $F_1, F_2,$ where each $F_i$ has size $n_i,$ branching factor $b_i,$ and nesting depth $d_i.$ Our task is to reorder the branches of $F_1$ and $F_2$ in a way that yields linearizations achieving the maximum sequence alignment. Let $n=\max(n_1, n_2),$ and define $b, d$ similarly. Our results are as follows: - A simple algorithm running in time $2^{O(bd)} n^2,$ establishing that the problem is fixed-parameter tractable (FPT) with respect to all four parameters $b_1,d_1, b_2, d_2.$ - An algorithm running in time $2^{O(bd_2)} n^7,$ showing that even when one of the functions has an unbounded nesting depth, the problem remains in FPT. - A hardness result showing that the problem is NP-hard even when constrained to constant $d_1, b_2, d_2.$ To the best of our knowledge, this is the first systematic study of function merging with branch reordering from an algorithmic or complexity-theoretic perspective.

cs.PL↗