Secure multi-party computation (MPC) protocols that are resilient to a dishonest majority allow the adversary to get the output of the computation while, at the same time, forcing the honest parties to abort. Aumann and Lindell introduced the enhanced notion of security with identifiable abort , which still allows the adversary to trigger an abort but, at the same time, it enables the honest parties to agree on the identity of the party that led to the abort. More recently, in Eurocrypt 2016, Garg et al. showed that, assuming access to a simultaneous message exchange channel for all the parties, at least four rounds of communication are required to securely realize non-trivial functionalities in the plain model. Following Garg et al., a sequence of works has matched this lower bound, but none of them achieved security with identifiable abort. In this work, we close this gap and show that four rounds of communication are also sufficient to securely realize any functionality with identifiable abort using standard and generic polynomial-time assumptions. To achieve this result we introduce the new notion of bounded-rewind secure MPC that guarantees security even against an adversary that performs a mild form of reset attacks. We show how to instantiate this primitive starting from any MPC protocol and by assuming trapdoor-permutations. The notion of bounded-rewind secure MPC allows for easier parallel composition of MPC protocols with other (interactive) cryptographic primitives. Therefore, we believe that this primitive can be useful in other contexts in which it is crucial to combine multiple primitives with MPC protocols while keeping the round complexity of the final protocol low.

Abstract Role-based access control (RBAC) has become a de facto standard to control access to restricted resources in complex systems and is widely deployed in many commercially available applications, including operating systems, databases and other softwares. The migration process towards RBAC, starting from the current access configuration, relies on the design of role mining techniques, whose aim is to define suitable roles that implement the given access policies. Some constraints can be used to transform the roles automatically output by the mining procedures and effectively capture the organization’s status under analysis. Such constraints can limit the final configuration characteristics, such as the number of roles assigned to a user, or the number of permissions included in a role, and produce a resulting role set that is effectively usable in real-world situations. In this paper, we consider two constraints: the number of permissions a role can include and the number of roles assigned to any user. In particular, we present two heuristics that produce roles compliant with both constraints and evaluate their performances using both real-world and synthetic datasets.

We extend the classical problem of privacy amplification to a setting where the active adversary, Eve, is also allowed tofully corruptthe internal memory (which includes the shared randomness, and local randomness tape) of one of the honest parties, Alice and Bob, before the execution of the protocol. We require that either one of Alice or Bob detects tampering, or they agree on a shared key that is indistinguishable from the uniform distribution to Eve. We obtain the following results: 1) we give a privacy amplification protocol via low-error non-malleable two-source extractors with one source having low min-entropy. In particular, this implies the existence of such (non-efficient) protocols; 2) we show that even slight improvements to the state-of-the-art explicit non-malleable two-source extractors would lead to explicit low-error, low min-entropy two-source extractors, thereby resolving a long-standing open question. This suggests that obtaining (information-theoretically secure)explicitnon-malleable two-source extractors for (1) might be hard; 3) we present explicit constructions of low-error, low min-entropy non-malleable two-source extractors in the CRS model of (Garg, Kalai, Khurana, Eurocrypt 2020), assuming either the quasi-polynomial hardness of DDH or the existence of nearly-optimal collision-resistant hash functions; 4) we instantiate our privacy amplification protocol with the above mentioned non-malleable two-source extractors in the CRS model, leading to explicit, computationally-secure protocols. This is not immediate from (1) because in the computational setting we need to make sure that, in particular, all randomness sources remain samplable throughout the proof. This requires upgrading the assumption of quasi-polynomial hardness of DDH to sub-exponential hardness of DDH.We emphasize that each of the first three results can be read independently.

In this paper, we propose an intelligent and private method to profile social network users. The scenario is constituted by the social network platform and an advertiser willing to expose its products to potentially interested users. The intelligence is in the use of text analysis services (like Wikify!) to extract knowledge from the social networks posts and get measure criteria to compare them, by using Rough Set Theory. The private layer guarantees that neither of the parties in the scenario can gain any advantage by knowing information (tastes, users topic adherence, etc...) of the other party.

Role Based Access Control (RBAC) models have been adopted in many organizations as the standard way to implement security policies and assign access to restricted resources to roles and roles to users. To capture the business relationships within the organization and efficiently migrate towards RBAC, several role mining techniques have been defined. Constraints on the resulting roles and assignments to users can be imposed to filter out inconsistent situations produced by the automatic algorithm and to better capture the status of the organization. In this paper we are interested in constraints on the number of permissions that can be included in a role and on the number of persons a role can be assigned to. We analyze the problem and propose a couple of heuristics. The heuristics have been applied to standard datasets to validate their performance.