Differential privacy has gained a lot of attention in recent years as a general model for the
protection of personal information when used and disclosed for secondary purposes. It has …

We consider how an untrusted data aggregator can learn desired statistics over multiple
participants' data, without compromising each individual's privacy. We propose a …

We consider applications where an untrusted aggregator would like to collect privacy
sensitive data from users, and compute aggregate statistics periodically. For example …

The proliferation and ever-increasing capabilities of mobile devices such as smart phones
give rise to a variety of mobile sensing applications. This paper studies how an untrusted …

We are all increasingly the subjects of data collection and processing systems that use data
generated both about and by us to provide and optimise a wide range of services. Means for …

This paper presents Orchard, a system that can answer queries about sensitive data that is
held by millions of user devices, with strong differential privacy guarantees. Orchard …

The proliferation and ever-increasing capabilities of mobile devices such as smart phones
give rise to a variety of mobile sensing applications. This paper studies how an untrusted …

Mobile phone sensing provides a promising paradigm for collecting sensing data and has
been receiving increasing attention in recent years. Different from most existing works, which …

Aggregate statistics computed from time-series data contributed by individual mobile nodes
can be very useful for many mobile sensing applications. Since the data from individual …

As a novel problem-solving paradigm, crowdsourcing has been emerged aiming at
performing truthful data aggregation and addressing problems that are hard for one …