14 research outputs found
Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users
We present a new concern when collecting data from individuals that arises
from the attempt to mitigate privacy leakage in multiple reporting: tracking of
users participating in the data collection via the mechanisms added to provide
privacy. We present several definitions for untrackable mechanisms, inspired by
the differential privacy framework.
Specifically, we define the trackable parameter as the log of the maximum
ratio between the probability that a set of reports originated from a single
user and the probability that the same set of reports originated from two users
(with the same private value). We explore the implications of this new
definition. We show how differentially private and untrackable mechanisms can
be combined to achieve a bound for the problem of detecting when a certain user
changed their private value.
Examining Google's deployed solution for everlasting privacy, we show that
RAPPOR (Erlingsson et al. ACM CCS, 2014) is trackable in our framework for the
parameters presented in their paper.
We analyze a variant of randomized response for collecting statistics of
single bits, Bitwise Everlasting Privacy, that achieves good accuracy and
everlasting privacy, while only being reasonably untrackable, specifically
grows linearly in the number of reports. For collecting statistics about data
from larger domains (for histograms and heavy hitters) we present a mechanism
that prevents tracking for a limited number of responses.
We also present the concept of Mechanism Chaining, using the output of one
mechanism as the input of another, in the scope of Differential Privacy, and
show that the chaining of an -LDP mechanism with an
-LDP mechanism is
-LDP
and that this bound is tight.Comment: 45 pages, 4 figures. To appear on FORC 202
The Role of Interactivity in Local Differential Privacy
We study the power of interactivity in local differential privacy. First, we
focus on the difference between fully interactive and sequentially interactive
protocols. Sequentially interactive protocols may query users adaptively in
sequence, but they cannot return to previously queried users. The vast majority
of existing lower bounds for local differential privacy apply only to
sequentially interactive protocols, and before this paper it was not known
whether fully interactive protocols were more powerful. We resolve this
question. First, we classify locally private protocols by their
compositionality, the multiplicative factor by which the sum of a
protocol's single-round privacy parameters exceeds its overall privacy
guarantee. We then show how to efficiently transform any fully interactive
-compositional protocol into an equivalent sequentially interactive protocol
with an blowup in sample complexity. Next, we show that our reduction is
tight by exhibiting a family of problems such that for any , there is a
fully interactive -compositional protocol which solves the problem, while no
sequentially interactive protocol can solve the problem without at least an
factor more examples. We then turn our attention to
hypothesis testing problems. We show that for a large class of compound
hypothesis testing problems --- which include all simple hypothesis testing
problems as a special case --- a simple noninteractive test is optimal among
the class of all (possibly fully interactive) tests
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
In this paper, we study the problem of publishing a stream of real-valued
data satisfying differential privacy (DP). One major challenge is that the
maximal possible value can be quite large; thus it is necessary to estimate a
threshold so that numbers above it are truncated to reduce the amount of noise
that is required to all the data. The estimation must be done based on the data
in a private fashion. We develop such a method that uses the Exponential
Mechanism with a quality function that approximates well the utility goal while
maintaining a low sensitivity. Given the threshold, we then propose a novel
online hierarchical method and several post-processing techniques.
Building on these ideas, we formalize the steps into a framework for private
publishing of stream data. Our framework consists of three components: a
threshold optimizer that privately estimates the threshold, a perturber that
adds calibrated noises to the stream, and a smoother that improves the result
using post-processing. Within our framework, we design an algorithm satisfying
the more stringent setting of DP called local DP (LDP). To our knowledge, this
is the first LDP algorithm for publishing streaming data. Using four real-world
datasets, we demonstrate that our mechanism outperforms the state-of-the-art by
a factor of 6-10 orders of magnitude in terms of utility (measured by the mean
squared error of answering a random range query)