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20212025
most citedEfficient Recovery of a Shared Secret via Cooperation: Applications to SDMM and PIR

7 citations · 9 across the 11 of their papers we have counts for

collaborators

11 papers

cs.IT2025

Analog Secure Distributed Matrix Multiplication

Okko Makkonen, Camilla Hollanti

In this paper, we present secure distributed matrix multiplication (SDMM) schemes over the complex numbers with good numerical stability and small mutual information leakage by uti…

cs.IT2025

A Matrix Completion Approach for the Construction of MDP Convolutional Codes

Sakshi Dang, Julia Lieb, Okko Makkonen +2

Maximum Distance Profile (MDP) convolutional codes are an important class of channel codes due to their maximal delay-constrained error correction capabilities. The design of MDP c…

cs.IT2024

Secret Sharing for Secure and Private Information Retrieval: A Construction Using Algebraic Geometry Codes

Okko Makkonen, David Karpuk, Camilla Hollanti

Private information retrieval (PIR) considers the problem of retrieving a data item from a database or distributed storage system without disclosing any information about which dat…

cs.IT2024

Algebraic Geometry Codes for Cross-Subspace Alignment in Private Information Retrieval

Okko Makkonen, David Karpuk, Camilla Hollanti

A new framework for interference alignment in secure and private information retrieval (PIR) from colluding servers is proposed, generalizing the original cross-subspace alignment…

cs.IT2024

Flexible Field Sizes in Secure Distributed Matrix Multiplication via Efficient Interference Cancellation

Okko Makkonen

In this paper, we propose a new secure distributed matrix multiplication (SDMM) scheme using the inner product partitioning. We construct a scheme with a minimal number of workers…

cs.LG2024

Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data

Okko Makkonen, Sampo Niemelä, Camilla Hollanti +1

This work focuses on the challenges of non-IID data and stragglers/dropouts in federated learning. We introduce and explore a privacy-flexible paradigm that models parts of the cli…

cs.IT2023★ 2 cited

Algebraic Geometry Codes for Secure Distributed Matrix Multiplication

Okko Makkonen, Elif Saçıkara, Camilla Hollanti

In this paper, we propose a novel construction for secure distributed matrix multiplication (SDMM) based on algebraic geometry (AG) codes, which we call the PoleGap SDMM scheme. Th…

cs.IT2022

Secure Distributed Gram Matrix Multiplication

Okko Makkonen, Camilla Hollanti

The Gram matrix of a matrix is defined as (or ). Computing the Gram matrix is an important operation in many applications, such as linear regression with the lea…

cs.IT2022

General Framework for Linear Secure Distributed Matrix Multiplication with Byzantine Servers

Okko Makkonen, Camilla Hollanti

In this paper, a general framework for linear secure distributed matrix multiplication (SDMM) is introduced. The model allows for a neat treatment of straggling and Byzantine serve…

cs.CR2022

Analog Secure Distributed Matrix Multiplication over Complex Numbers

Okko Makkonen, Camilla Hollanti

This work considers the problem of distributing matrix multiplication over the real or complex numbers to helper servers, such that the information leakage to these servers is clos…

cs.IT2021★ 7 cited

Efficient Recovery of a Shared Secret via Cooperation: Applications to SDMM and PIR

Jie Li, Okko Makkonen, Camilla Hollanti +1

This work considers the problem of privately outsourcing the computation of a matrix product over a finite field to helper servers. These servers are considered…