text: Minor corrections

text/related/statistical.tex

@@ -238,7 +238,7 @@ However, the framework does not take into account privacy leakage stemming from
 % - perturbation (Laplace)
 \hypertarget{bolot2013private}{Bolot et al.}~\cite{bolot2013private} introduce the notion of \emph{decayed privacy} in continual observation of aggregates (sums).
 The authors recognize the fact that monitoring applications focus more on recent events, and data, therefore, the value of previous data releases exponentially fades.
-This leads to a schema of privacy with expiration, according to which, recent events, and data are more privacy sensitive than those preceding.
+This leads to a schema of privacy with expiration, according to which, recent events, and data are more privacy-sensitive than those preceding.
 Based on this, they apply decayed sum functions for answering sliding window queries of fixed window size $w$ on data streams.
 Namely, window sum compute the difference of two running sums, and exponentially decayed and polynomial decayed sums estimate the sum of decayed data.
 For every consecutive $w$ data points the algorithm generates binary trees where each node is perturbed with Laplace noise with scale proportional to $w$.
@@ -409,7 +409,7 @@ RPTR adapts the rate with which it samples data according to the accuracy with w
 Before releasing data statistics, the mechanism perturbs the original values with Laplacian noise the impact of which is mitigated by using Ensemble Kalman filtering.
 The combination of adaptive sampling and filtering can improve the accuracy when predicting the values of non-sampled data points, and thus saving more privacy budget (i.e.,~higher data utility) for data points that the mechanism decides to release.
 The mechanism detects highly frequented map regions and, using a quad-tree, it calculate the each region's privacy weight.
-In their implementation, the authors assume that highly frequented regions tend to be more privacy sensitive, and thus more noise (i.e.,~less privacy budget to invest) needs to be introduced before publicly releasing the users' data falling into these regions.
+In their implementation, the authors assume that highly frequented regions tend to be more privacy-sensitive, and thus more noise (i.e.,~less privacy budget to invest) needs to be introduced before publicly releasing the users' data falling into these regions.
 The efficiency (both in terms of user privacy and data utility) of the mechanism depends on the number of regions that it divides the map, and therefore the challenge of its optimal division is an interesting future research topic.
 
 % Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon