Jäger: Automated Telephone Call Traceback

• URL: http://arxiv.org/abs/2409.02839v4
• Date: Tue, 17 Sep 2024 17:51:43 GMT
• Title: Jäger: Automated Telephone Call Traceback
• Authors: David Adei, Varun Madathil, Sathvik Prasad, Bradley Reaves, Alessandra Scafuro,
• Abstract summary: We introduce J"ager, a distributed secure call traceback system. J"ager can trace a call in a few seconds, even with partial deployment.
• Score: 45.67265362470739
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsolicited telephone calls that facilitate fraud or unlawful telemarketing continue to overwhelm network users and the regulators who prosecute them. The first step in prosecuting phone abuse is traceback -- identifying the call originator. This fundamental investigative task currently requires hours of manual effort per call. In this paper, we introduce J\"ager, a distributed secure call traceback system. J\"ager can trace a call in a few seconds, even with partial deployment, while cryptographically preserving the privacy of call parties, carrier trade secrets like peers and call volume, and limiting the threat of bulk analysis. We establish definitions and requirements of secure traceback, then develop a suite of protocols that meet these requirements using witness encryption, oblivious pseudorandom functions, and group signatures. We prove these protocols secure in the universal composibility framework. We then demonstrate that J\"ager has low compute and bandwidth costs per call, and these costs scale linearly with call volume. J\"ager provides an efficient, secure, privacy-preserving system to revolutionize telephone abuse investigation with minimal costs to operators.

Related papers

• Characterizing Robocalls with Multiple Vantage Points [50.423738777985314]
  We show that unsolicited calls are in slow decline, though complaints and call volumes remain high. We find that robocallers have managed to adapt to STIR/SHAKEN, a mandatory call authentication scheme. In total, our findings highlight the most promising directions for future efforts to characterize and stop telephone spam.
  arXiv  Detail & Related papers  (2024-10-22T18:54:12Z)
• Combating Phone Scams with LLM-based Detection: Where Do We Stand? [1.8979188847659796]
  This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls. LLMs-based detectors can identify potential scams as they occur, offering immediate protection to users.
  arXiv  Detail & Related papers  (2024-09-18T02:14:30Z)
• Developing a Production System for Purpose of Call Detection in Business Phone Conversations [1.4450257955652834]
  We describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time.
  arXiv  Detail & Related papers  (2022-05-13T21:45:54Z)
• Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain [80.24811082454367]
  We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks. We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them.
  arXiv  Detail & Related papers  (2021-09-12T12:44:52Z)
• BeeTrace: A Unified Platform for Secure Contact Tracing that Breaks Data Silos [73.84437456144994]
  Contact tracing is an important method to control the spread of an infectious disease such as COVID-19. Current solutions do not utilize the huge volume of data stored in business databases and individual digital devices. We propose BeeTrace, a unified platform that breaks data silos and deploys state-of-the-art cryptographic protocols to guarantee privacy goals.
  arXiv  Detail & Related papers  (2020-07-05T10:33:45Z)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)
• Cross Hashing: Anonymizing encounters in Decentralised Contact Tracing Protocols [0.76146285961466]
  App-based solutions have emerged for Contact Tracing, including a protocol designed by Apple and Google. This protocol contains two well-documented de-anonymisation attacks. We propose a novel "cross hashing" approach to cryptographically guarantee minimum exposure durations.
  arXiv  Detail & Related papers  (2020-05-26T17:20:25Z)
• Decentralized Privacy-Preserving Proximity Tracing [50.27258414960402]
  DP3T provides a technological foundation to help slow the spread of SARS-CoV-2. System aims to minimise privacy and security risks for individuals and communities.
  arXiv  Detail & Related papers  (2020-05-25T12:32:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.