privy: Privacy Preserving Collaboration Across Multiple Service Providers to Combat Telecom Spams

Abstract

Nuisance or unsolicited calls and instant messages come at any time in a variety of different ways. These calls would not only exasperate recipients with the unwanted ringing, impacting their productivity, but also lead to a direct financial loss to users and service providers. Telecommunication Service Providers (TSPs) often employ standalone detection systems to classify call originators as spammers or non-spammers using their behavioral patterns. These approaches perform well when spammers target a large number of recipients of one service provider. However, professional spammers try to evade the standalone systems by intelligently reducing the number of spam calls sent to one service provider, and instead distribute calls to the recipients of many service providers. Naturally, collaboration among service providers could provide an effective defense, but it brings the challenge of privacy protection and system resources required for the collaboration process. In this paper, we propose a novel decentralized collaborative system named privy for the effective blocking of spammers who target multiple TSPs. More specifically, we develop a system that aggregates the feedback scores reported by the collaborating TSPs without employing any trusted third party system, while preserving the privacy of users and collaborators. We evaluate the system performance of privy using both the synthetic and real call detail records. We find that privy can correctly block spammers in a quicker time, as compared to standalone systems. Further, we also analyze the security and privacy properties of the privy system under different adversarial models.

Layer-based Privacy and Security Architecture for Cloud Data Sharing

“…This method is used to preserve the data from unauthorized disclosure during transmission. The aim of the method is to make the data difficult to understand by malicious entities [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]. Kao et.…”

“…al. [34], [35] for the effective blocking of spammers who target multiple TSPs. The systems have evaluated the performance using the synthetic and real call detail records.…”

PriVeto: a fully private two‐round veto protocol

“…The establishment of pairwise secure channels is complex to realize in practice [19]. For these reasons, researchers normally propose specific MPC solutions to efficiently solve specific problems, e.g., e-voting [18], secure network statistics aggregation [1,2,8,16], secure function computation [12] and secure auctions [23] etc.…”

PrivBox: Verifiable decentralized reputation system for online marketplaces

“…The PrivBox system can also be applied in other domains such as collaborative intrusion detection systems [26,27], where multiple Internet service providers could collaborate for the effective and early identification of attackers; collaborative filtering of spammers in online social networks, where multiple service providers collaborate with each other [28,29,30,31] for the quick detection of stealthy and smart spammers; and the private statistics aggregation in the private and secure surveys [32,33]. Currently, the trustworthiness of a cloud marketplace is estimated based on the service level agreement by the cloud service provider; however, it is necessary that the trustworthiness estimation should be take into account feedback from consumers of the cloud provider.…”