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Rawane Issa

dblp:211/5012 · DBLP profile ↗
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3ranked-venue papers
1as first author
2since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Cryptographic protocols and secure computation · 75% Usable security · 25%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usable security › security operations
abuse reporting
0.612022
Hecate: Abuse Reporting in Secure Messengers with Sealed Sender · USENIX Security Symposium 2022
Cryptographic protocols and secure computation › private information retrieval
batch private information retrieval
0.612022
Batched Differentially Private Information Retrieval · USENIX Security Symposium 2022
Cryptographic protocols and secure computation
private information retrieval
0.612022
Batched Differentially Private Information Retrieval · USENIX Security Symposium 2022

Methods — techniques the papers use, named apart from their topics

differential privacy · 0.6
YearPublicationVenuePosition
2022 Batched Differentially Private Information Retrieval
Kinan Dak Albab, Rawane Issa, Mayank Varia, Kalman Graffi
USENIX Security Symposium2
2022 Hecate: Abuse Reporting in Secure Messengers with Sealed Sender
Rawane Issa, Nicolas Alhaddad, Mayank Varia
USENIX Security Symposium1
2018 Accessible Privacy-Preserving Web-Based Data Analysis for Assessing and Addressing Economic Inequalities
abstract
An essential component of initiatives that aim to address pervasive inequalities of any kind is the ability to collect empirical evidence of both the status quo baseline and of any improvement that can be attributed to prescribed and deployed interventions. Unfortunately, two substantial barriers can arise preventing the collection and analysis of such empirical evidence: (1) the sensitive nature of the data itself and (2) a lack of technical sophistication and infrastructure available to both an initiative's beneficiaries and to those spearheading it. In the last few years, it has been shown that a cryptographic primitive called secure multi-party computation (MPC) can provide a natural technological resolution to this conundrum. MPC allows an otherwise disinterested third party to contribute its technical expertise and resources, to avoid incurring any additional liabilities itself, and (counterintuitively) to reduce the level of data exposure that existing parties must accept to achieve their data analysis goals. However, achieving these benefits requires the deliberate design of MPC tools and frameworks whose level of accessibility to non-technical users with limited infrastructure and expertise is state-of-the-art. We describe our own experiences designing, implementing, and deploying such usable web applications for secure data analysis within the context of two real-world initiatives that focus on promoting economic equality.
Andrei Lapets, Frederick Jansen, Kinan Dak Albab, Rawane Issa, Lucy Qin, Mayank Varia, Azer Bestavros
COMPASS4