From: ~Vaibhav_Kulkarni1
comment: We thank the reviewer for the detailed and helpful comments. We will make sure to take them into consideration in the next iteration of this manuscript. For now, please find our responses below:

RE Evaluation: Please note that in the abstract we state both 1) and 2) are desired characteristics of trajectory generative models, however, we do not claim that the proposed methods will deliver on the two fronts. The question we are trying to answer is “Can deep generative models synthesize realistic trajectories while preserving privacy?” and this is what we benchmark and evaluate. 

RE Technical Comments:
1) We agree that deep models are still somewhat in the "grey" area between parametric/nonparametric approaches, and this question is still debatable in the community. We followed the terminology used in [1] where GANs were considered as nonparametric. Our initial thought for selecting the "nonparametric" terminology was that we do not have prior assumptions about the data distribution, however, we will modify the title to avoid any ambiguities. 

2) We would be happy to elaborate and add more details on the metrics and methods used, but in the limited space we tried to add what we perceive as the essential information and proper references for further reading. Additionally, we did not want to favor any of the benchmarked methods, and adding more details solely for copulas seemed as a somewhat biased presentation.

Regarding nonparametric copulas and privacy, again, we do not state they are capable of preserving privacy, this is what we evaluate. As the reviewer says, it does seem like copulas are more vulnerable in terms of privacy and this is what we introduce as a question to be explored in future work. 

3) We use the hashed version instead of the raw location coordinates in order to avoid numerical instabilities. The raw data has a high granularity (< 1 m^2 with latitude and longitude coordinates represented to accuracy of 15 decimal digits). We therefore configure the google S2 library [2] to project each coordinate pair onto a cell of dimension 3m^2. This choice is motivated by the localization accuracy of a typical GPS sensor and the performance complexity involved when subdividing the cells to the leaf level [3]. 

4) We agree with this point, but as mentioned before we unfortunately did not have enough space to add more baselines. We will surely add this to future extended versions of the abstract or add at least one parametric approach for a camera ready version should the abstract be accepted.

5) As mentioned, the question of interest is "Can (deep) generative models synthesize realistic trajectories while preserving privacy?". To the best of our knowledge this has not been explored before and so, with this initial contribution we want to turn the attention of the community towards this topic which we deem as important.

Having said this, we would like to add that the privacy score has not been used for evaluating privacy leakage associated with trajectories in prior work. Therefore, the results in Figure 2 only shed some light into this issue and further investigation is needed (we mention potential directions in the conclusion and future work).

Regarding Figure 2c, we note that even real-world trajectories exhibit such behaviors as off-road and sharp edges, which is a consequence of a “dirty dataset”( i.e. the data we use to train the generative models itself contains such irregularities) and errors associated with GPS sensors, hence this is what the methods pick up. We can add an instance of the trajectory belonging to the original dataset to highlight that such instances are a part of the training set.

[1] Ouyang, Kun, et al. "A Non-Parametric Generative Model for Human Trajectories." International Joint Conference on Artificial Intelligence. 2018
[2] https://s2geometry.io/resources/s2cell_statistics
[3] Jennings, Denise, et al. "The validity and reliability of GPS units for measuring distance in team sport specific running patterns." International journal of sports physiology and performance 5.3 (2010): 328-341

Many thanks again for your time and insightful feedback. Were you to need further information, please feel free to come back to us.

title: Clarifications

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper40/AnonReviewer3
relevance: 3: borderline
impact: 2: low
novelty: 2: low

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper40/AnonReviewer3
evaluation: The Authors propose and compare an array of generative models to synthesize human mobility trajectories. The underlying hypothesis is that deep generative models can capture the dependencies of human trajectories and can produce hallucinated samples from the trajectory distribution  that are 1) realistic but 2) still preserve user privacy.


Technical comments:
1) The title of the paper mentions non-parametric approaches. However, the majority of approaches are parametric (deep) models. 
2) While this is an extended-abstract-like paper, it is very light on the details.
Key aspects, such as the evaluations metrics and the most promising method (copulas) are described vaguely, making it hard to judge their suitability in this case.
In particular, I find it hard to understand how a non-parametric method (copulas), where "raw" data is used explicitly, can preserve user privacy (the results in Figure 2b reaffirms this intuition). 
3) Data representation - It was not clear from the text why data had to be hashed and not used as is. Is it really just a dimensionality reduction from 3D to 2D? 
4) Results - While the authors mention other approaches for trajectory generation, none are used to benchmark the proposed approaches. This type of comparison is critical, especially given the shortcomings of existing methods described by the authors.
5) In the beginning of the paper, the authors hypothesize that deep models produce realistic trajectories while still preserving user privacy. Figure 2b provides contradicting results where privacy scores are far from 0. The authors argue that this might be a result of biased privacy scoring methods. However, weren't similar privacy scoring methods used to assess past methods? Furthermore, sampled trajectories, Figure 2c, are far from realistic (off-road, sharp transitions etc.). While this might be better than the state-of-the-art, it is hard to assess without a proper comparison in the text.