Existing End-to-End secure messaging applications trust a single service provider to deliver messages in a consistent order to a consistent group of conversation members. We propose a protocol that removes this single point of failure by using multiple service providers, enforcing conversation integrity as long as one service provider out of N behave honestly. However, this approach could potentially increase the number of entities that learn the metadata for a conversation. In this work we discuss the challenges and provide a protocol that limits the metadata leakage to that of existing messaging applications while still providing strong conversation integrity.
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Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog
We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9-14% over using message text only.
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- Award ID(s):
- 1815291
- NSF-PAR ID:
- 10111348
- Date Published:
- Journal Name:
- Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
