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We present a new concern when collecting data from individuals that arisesfrom the attempt to mitigate privacy leakage in multiple reporting: tracking ofusers participating in the data collection via the mechanisms added to provideprivacy. We present several definitions for untrackable mechanisms, inspired bythe differential privacy framework.  Specifically, we define the trackable parameter as the log of the maximumratio between the probability that a set of reports originated from a singleuser and the probability that the same set of reports originated from two users(with the same private value). We explore the implications of this newdefinition. We show how differentially private and untrackable mechanisms canbe combined to achieve a bound for the problem of detecting when a certain userchanged their private value.  Examining Google's deployed solution for everlasting privacy, we show thatRAPPOR (Erlingsson et al. ACM CCS, 2014) is trackable in our framework for theparameters presented in their paper.  We analyze a variant of randomized response for collecting statistics ofsingle bits, Bitwise Everlasting Privacy, that achieves good accuracy andeverlasting privacy, while only being reasonably untrackable, specificallygrows linearly in the number of reports. For collecting statistics about datafrom larger domains (for histograms and heavy hitters) we present a mechanismthat prevents tracking for a limited number of responses.  We also present the concept of Mechanism Chaining, using the output of onemechanism as the input of another, in the scope of Differential Privacy, andshow that the chaining of an $\varepsilon_1$-LDP mechanism with an$\varepsilon_2$-LDP mechanism is$\ln\frac{e^{\varepsilon_1+\varepsilon_2}+1}{e^{\varepsilon_1}+e^{\varepsilon_2}}$-LDPand that this bound is tight.

Can Two Walk Together: Privacy Enhancing Methods and Preventing Tracking of Users