Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy

Fairness concerns about algorithmic decision-making systems have been mainly focused on the outputs (e.g., the accuracy of a classifier across individuals or groups). However, one may additionally be concerned with fairness in the inputs. In this paper, we propose and formulate two properties regarding the inputs of (features used by) a classifier. In particular, we claim that fair privacy (whether individuals are all asked to reveal the same information) and need-to-know (whether users are only asked for the minimal information required for the task at hand) are desirable properties of a decision system. We explore the interaction between these properties and fairness in the outputs (fair prediction accuracy). We show that for an optimal classifier these three properties are in general incompatible, and we explain what common properties of data make them incompatible. Finally we provide an algorithm to verify if the trade-off between the three properties exists in a given dataset, and use the algorithm to show that this trade-off is common in real data

PKI Interoperability: Still an Issue? A Solution in the X. 509 Realm

There exist many obstacles that slow the global adoption of public key infrastructure (PKI) technology. The PKI interoperability problem, being poorly understood, is one of the most confusing. In this paper, we clarify the PKI interoperability issue by exploring both the juridical and technical domains. We demonstrate the origin of the PKI interoperability problem by determining its root causes, the latter being legal, organizational and technical differences between countries, which mean that relying parties have no one to rely on. We explain how difficult it is to harmonize them. Finally, we propose to handle the interoperability problem from the trust management point of view, by introducing the role of a trust broker which is in charge of helping relying parties make informed decisions about X.509 certificates

TKSE: Trustworthy keyword search over encrypted data with two-side verifiability via blockchain

AXA Research Fund Singapor

TumbleBit: an untrusted Bitcoin-compatible anonymous payment hub

This paper presents TumbleBit, a new unidirectional unlinkable payment hub that is fully compatible with today s Bitcoin protocol. TumbleBit allows parties to make fast, anonymous, off-blockchain payments through an untrusted intermediary called the Tumbler. TumbleBits anonymity properties are similar to classic Chaumian eCash: no one, not even the Tumbler, can link a payment from its payer to its payee. Every payment made via TumbleBit is backed by bitcoins, and comes with a guarantee that Tumbler can neither violate anonymity, nor steal bitcoins, nor print money by issuing payments to itself. We prove the security of TumbleBit using the real/ideal world paradigm and the random oracle model. Security follows from the standard RSA assumption and ECDSA unforgeability. We implement TumbleBit, mix payments from 800 users and show that TumbleBits offblockchain payments can complete in seconds.https://eprint.iacr.org/2016/575.pdfPublished versio