activity
20182022
most citedTowards Formalizing the GDPR's Notion of Singling Out

91 citations · 111 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CR2022

Control, Confidentiality, and the Right to be Forgotten

Aloni Cohen, Adam Smith, Marika Swanberg +1

Recent digital rights frameworks give users the right to delete their data from systems that store and process their personal information (e.g., the "right to be forgotten" in the…

cs.CY2022★ 2 cited

Can the Government Compel Decryption? Don't Trust -- Verify

Aloni Cohen, Sarah Scheffler, Mayank Varia

If a court knows that a respondent knows the password to a device, can the court compel the respondent to enter that password into the device? In this work, we propose a new approa…

cs.CY2022★ 9 cited

Census TopDown: The Impacts of Differential Privacy on Redistricting

Aloni Cohen, Moon Duchin, JN Matthews +1

The 2020 Decennial Census will be released with a new disclosure avoidance system in place, putting differential privacy in the spotlight for a wide range of data users. We conside…

cs.CR2022★ 5 cited

Attacks on Deidentification's Defenses

Aloni Cohen

Quasi-identifier-based deidentification techniques (QI-deidentification) are widely used in practice, including -anonymity, -diversity, and -closeness. We present three…

cs.CY2019★ 91 cited

Towards Formalizing the GDPR's Notion of Singling Out

Aloni Cohen, Kobbi Nissim

There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to which technical offerings adequately match expect…

cs.CR2018

Linear Program Reconstruction in Practice

Aloni Cohen, Kobbi Nissim

We briefly report on a successful linear program reconstruction attack performed on a production statistical queries system and using a real dataset. The attack was deployed in tes…

cs.LG2018★ 4 cited

From Soft Classifiers to Hard Decisions: How fair can we be?

Ran Canetti, Aloni Cohen, Nishanth Dikkala +3

A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a non-binary "scoring" classifier that is calib…