Distributed Privacy-Preserving Policy Reconciliation

Abstract

Abstract-Organizations use security policies to regulate how they share and exchange information, e.g., under what conditions data can be exchanged, what protocols are to be used, who is granted access, etc. Agreement on specific policies is achieved though policy reconciliation, where multiple parties, with possibly different policies, exchange their security policies, resolve differences, and reach a consensus. Current solutions for policy reconciliation do not take into account the privacy concerns of reconciliating parties. This paper addresses the problem of preserving privacy during security policy reconciliation. We introduce new protocols that meet the privacy requirements of the organizations and allow parties to find a common policy rule which maximizes their individual preferences.

Enabling fair and privacy-preserving applications using reconciliation protocols on ordered sets

“…In their work, Kissner and Song also define the privacy-preserving composition of these basic operations. Independently, two-party, fair and privacy-preserving reconciliation protocols for ordered sets were introduced in [3], [1]. The work by Neugebauer et al [2] leverages both lines of work introducing multi-party (i.e., for two or more parties) privacy-preserving reconciliation on ordered sets (MPROS) crafting specific multisets.…”

“…As first described in [3], PROS enables the process of fair and privacy-preserving policy reconciliation (PPPR). Policy reconciliation assumes n parties with their individual, in general different, set of rules which define parameters for collaboration or information exchange with others.…”

“…In [4], Kissner and Song present privacy-preserving set operations for multisets 1 . The operations include union, intersection, as well as element reduction.…”

“…Recently, multi-party protocols for enabling fair and privacy-preserving reconciliation of ordered sets have been introduced [1], [2]. In this context, privacy-preserving refers to the fact that the parties' input sets remain private during and after protocol execution and only the result of the reconciliation is made known to all legitimate parties involved in this process.…”

“…In particular, the reconciliation is optimized according to some notion of fairness which was agreed upon by all parties prior to the reconciliation process. Examples of such notions of fairness were introduced in [3] and include the sum of ranks and the minimum of ranks. In case of the former, the sum of the preferences of all parties is optimized while the latter maximizes the lowest of the preferences of all parties involved.…”