1,933 research outputs found
Heavy Hitters and the Structure of Local Privacy
We present a new locally differentially private algorithm for the heavy
hitters problem which achieves optimal worst-case error as a function of all
standardly considered parameters. Prior work obtained error rates which depend
optimally on the number of users, the size of the domain, and the privacy
parameter, but depend sub-optimally on the failure probability.
We strengthen existing lower bounds on the error to incorporate the failure
probability, and show that our new upper bound is tight with respect to this
parameter as well. Our lower bound is based on a new understanding of the
structure of locally private protocols. We further develop these ideas to
obtain the following general results beyond heavy hitters.
Advanced Grouposition: In the local model, group privacy for
users degrades proportionally to , instead of linearly in
as in the central model. Stronger group privacy yields improved max-information
guarantees, as well as stronger lower bounds (via "packing arguments"), over
the central model.
Building on a transformation of Bassily and Smith (STOC 2015), we
give a generic transformation from any non-interactive approximate-private
local protocol into a pure-private local protocol. Again in contrast with the
central model, this shows that we cannot obtain more accurate algorithms by
moving from pure to approximate local privacy
Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data
Operational network data, management data such as customer care call logs and
equipment system logs, is a very important source of information for network
operators to detect problems in their networks. Unfortunately, there is lack of
efficient tools to automatically track and detect anomalous events on
operational data, causing ISP operators to rely on manual inspection of this
data. While anomaly detection has been widely studied in the context of network
data, operational data presents several new challenges, including the
volatility and sparseness of data, and the need to perform fast detection
(complicating application of schemes that require offline processing or
large/stable data sets to converge).
To address these challenges, we propose Tiresias, an automated approach to
locating anomalous events on hierarchical operational data. Tiresias leverages
the hierarchical structure of operational data to identify high-impact
aggregates (e.g., locations in the network, failure modes) likely to be
associated with anomalous events. To accommodate different kinds of operational
network data, Tiresias consists of an online detection algorithm with low time
and space complexity, while preserving high detection accuracy. We present
results from two case studies using operational data collected at a large
commercial IP network operated by a Tier-1 ISP: customer care call logs and
set-top box crash logs. By comparing with a reference set verified by the ISP's
operational group, we validate that Tiresias can achieve >94% accuracy in
locating anomalies. Tiresias also discovered several previously unknown
anomalies in the ISP's customer care cases, demonstrating its effectiveness
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