arXiv subjects

Peeter Laud

Publications and source records attributed to Peeter Laud.

10 recordsLinked to original sources

arXiv2026

Modal Logic for Distributed Trust

We propose a method for reasoning about trust in multi-agent systems, specifying a language for describing communication protocols and making trust assumptions and derivations. This is given an interpretation in a modal logic for describing the beliefs and communications of agents in a network. We define how information in the network can be shared via forwarding, and how trust between agents can be generalized to trust across networks. We give specifications for the modal logic which can be readily adapted into a lambda calculus of proofs. We show that by nesting modalities, we can describe chains of communication between agents, and establish suitable notions of trust for such chains. We see how this can be applied to trust models in public key infrastructures, as well as other interaction protocols in distributed systems.

cs.LO↗

arXiv2025

Zero-Knowledge Proof-of-Location Protocols for Vehicle Subsidies and Taxation Compliance

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for (electric) vehicle (EV) subsidies, using Zero-Knowledge Proofs (ZKPs). We present the design and evaluation of a Zero-Knowledge Proof-of-Location (ZK-PoL) system that ensures a vehicle's adherence to territorial driving requirements without disclosing specific location data, hence maintaining user privacy. Our findings suggest a promising approach to apply ZK-PoL protocols in large-scale governmental subsidy or taxation programs.

cs.CR↗

arXiv2024

Privacy-preserving server-supported decryption

In this paper, we consider encryption systems with two-out-of-two threshold decryption, where one of the parties (the client) initiates the decryption and the other one (the server) assists. Existing threshold decryption schemes disclose to the server the ciphertext that is being decrypted. We give a construction, where the identity of the ciphertext is not leaked to the server, and the client's privacy is thus preserved. While showing the security of this construction, we run into the issue of defining the security of a scheme with blindly assisted decryption. We discuss previously proposed security definitions for similar cryptographic functionalities and argue why they do not capture the expected meaning of security. We propose an ideal functionality for the encryption with server-supported blind threshold decryption in the universal composability model, carefully balancing between the meaning of privacy, and the ability to implement it. We construct a protocol and show that it is a secure implementation of the proposed functionality in the random oracle model.

cs.CR↗

arXiv2022

A Parallel Privacy-Preserving Shortest Path Protocol from a Path Algebra Problem

In this paper, we present a secure multiparty computation (SMC) protocol for single-source shortest distances (SSSD) in undirected graphs, where the location of edges is public, but their length is private. The protocol works in the Arithmetic Black Box (ABB) model on top of the separator tree of the graph, achieving good time complexity if the subgraphs of the graph have small separators (which is the case for e.g. planar graphs); the achievable parallelism is significantly higher than that of classical SSSD algorithms implemented on top of an ABB. We implement our protocol on top of the Sharemind MPC platform, and perform extensive benchmarking over different network environments. We compare our algorithm against the baseline picked from classical algorithms - privacy-preserving Bellman-Ford algorithm (with public edges).

cs.CR↗

arXiv2022

ZK-SecreC: a Domain-Specific Language for Zero Knowledge Proofs

We present ZK-SecreC, a domain-specific language for zero-knowledge proofs. We present the rationale for its design, its syntax and semantics, and demonstrate its usefulness on the basis of a number of non-trivial examples. The design features a type system, where each piece of data is assigned both a confidentiality and an integrity type, which are not orthogonal to each other. We perform an empiric evaluation of the statements produced by its compiler in terms of their size. We also show the integration of the compiler with the implementation of a zero-knowledge proof technique, and evaluate the running time of both Prover and Verifier.

cs.PL↗

arXiv2021

Content Confidentiality in Named Data Networking

In this paper we present the design of name based access control scheme which facilitates data confidentiality by applying end-to-end encryption to data published on NDN with flexible fine-grained access control, which allows to define an enforce access policies on published data. The scheme is based on ciphertext-policy attribute-based encryption (CP-ABE). We discuss the use of the scheme on the basis of two use-cases, and report overhead associated with it, based on our implementation.

cs.CR↗

arXiv2019

Secure Multi-Party Computation for Inter-Organizational Process Mining

Process mining is a family of techniques for analysing business processes based on event logs extracted from information systems. Mainstream process mining tools are designed for intra-organizational settings, insofar as they assume that an event log is available for processing as a whole. The use of such tools for inter-organizational process analysis is hampered by the fact that such processes involve independent parties who are unwilling to, or sometimes legally prevented from, sharing detailed event logs with each other. In this setting, this paper proposes an approach for constructing and querying a common type of artifact used for process mining, namely the frequency and time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging to different parties, in such a way that the parties do not share the event logs with each other. The proposal leverages an existing platform for secure multi-party computation, namely Sharemind. Since a direct implementation of DFG construction in Sharemind suffers from scalability issues, the paper proposes to rely on vectorization of event logs and to employ a divide-and-conquer scheme for parallel processing of sub-logs. The paper reports on an experimental evaluation that tests the scalability of the approach on real-life logs.

cs.CR↗

arXiv2019

Interpreting Epsilon of Differential Privacy in Terms of Advantage in Guessing or Approximating Sensitive Attributes

There are numerous methods of achieving $\epsilon$-differential privacy (DP). The question is what is the appropriate value of $\epsilon$, since there is no common agreement on a "sufficiently small" $\epsilon$, and its goodness depends on the query as well as the data. In this paper, we show how to compute $\epsilon$ that corresponds to $\delta$, defined as the adversary's advantage in probability of guessing some specific property of the output. The attacker's goal can be stated as Boolean expression over guessing particular attributes, possibly within some precision. The attributes combined in this way should be independent. We assume that both the input and the output distributions have corresponding probability density functions, or probability mass functions.

cs.CR↗

arXiv2019

Business Process Privacy Analysis in Pleak

Pleak is a tool to capture and analyze privacy-enhanced business process models to characterize and quantify to what extent the outputs of a process leak information about its inputs. Pleak incorporates an extensible set of analysis plugins, which enable users to inspect potential leakages at multiple levels of detail.

cs.CR↗

arXiv2018

Achieving Differential Privacy using Methods from Calculus

We introduce derivative sensitivity, an analogue to local sensitivity for continuous functions. We use this notion in an analysis that determines the amount of noise to be added to the result of a database query in order to obtain a certain level of differential privacy, and demonstrate that derivative sensitivity allows us to employ powerful mechanisms from calculus to perform the analysis for a variety of queries. We have implemented the analyzer and evaluated its efficiency and precision. We also show the flexibility of derivative sensitivity in specifying the quantitative privacy notion of the database, as desired by the data owner. Instead of only using the `number of changed rows' metric, our metrics can depend on the locations and amounts of changes in a much more nuanced manner. This will help to make sure that the distance is not larger than the data owner desires (which would undermine privacy), thereby encouraging the adoption of differentially private data analysis mechanisms.

cs.CR↗