esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Differences in Participants' Perceived Safety of Different Robot Light Colors Post-Interaction  
A one-way analysis of variance (one-way ANOVA) was used to examine the discrepancies in the participants' perceived safety of five different robot light colors after interactions, as illustrated in Table 5. The results indicated notable differences in perceived safety among the five light colors (\(F=62.325;p<0.05\)), suggesting that post hoc analyses could be conducted.  
Tukey post hoc tests were executed to compare participants' perceived safety regarding the five different robot lighting colors, as depicted in Table 6. The findings indicated significant variations in perceived safety between white (M = 3.908) \(>\) red (M = 1.933), white (M = 3.908) \(>\) green (M = 2.375), white (M = 3.908) \(>\) blue (M = 3.175), blue (M = 3.175) \(>\) red (M = 1.933), yellow (M = 4.183) \(>\) red (M = 1.933), green (M = 2.375) \(>\) red (M = 1.933), blue (M = 3.175) \(>\) green (M = 2.375), yellow (M = 4.183) \(>\) green (M = 2.375), and yellow (M = 4.183) \(>\) blue (M = 3.175), all showing significance (\(p<0.05\)). However, no significant difference was observed between white and yellow (\(p>0.05\)). This indicated that red (M = 1.933) elicited the lowest perceived safety, while white (M = 3.908) and yellow (M = 4.183) elicited the highest perceived safety.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lighting Color 1** & **Lighting Color 2** & **Lighting Color 1** & **Lighting Color 2** & **Mean Difference** & \(p\) \\  & & **Mean** & **Mean** & **Mean** & **Mean Difference** & \(p\) \\ \hline White & Red & 3.908 & 1.933 & 1.975 & 0.000 *** \\ White & Green & 3.908 & 2.375 & 1.533 & 0.000 *** \\ White & Blue & 3.908 & 3.175 & 0.733 & 0.000 *** \\ White & Yellow & 3.908 & 4.183 & \(-\)0.275 & 0.117 \\ Red & Green & 1.933 & 2.375 & \(-\)0.442 & 0.012 ** \\ Red & Blue & 1.933 & 3.175 & \(-\)1.242 & 0.000 *** \\ Red & Yellow & 1.933 & 4.183 & \(-\)2.25 & 0.000 *** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tukey post hoc test results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Lighting Color (Mean \(\pm\) SD)** & **F** & \(p\) \\ \hline
**White Lighting** & **Red Lighting** & **Green Lighting** & **Blue Lighting** & **Yellow Lighting** & **F** & \(p\) \\ \hline
3.908 \(\pm\) 0.677 & 1.933 \(\pm\) 0.619 & 2.375 \(\pm\) 0.814 & 3.175 \(\pm\) 0.717 & 4.183 \(\pm\) 0.517 & 61.007 & 0.000 *** \\ \hline \hline \end{tabular} Note: *** \(p<0.001\).  
\end{table}
Table 5: One-way ANOVA results.
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 4 Our Approach
### Preprocessing  
Preprocessing of bug reports is a critical step in preparing the textual data for feature extraction and analysis. Bug reports, which primarily consist of textual descriptions, are processed to extract meaningful words and phrases through several preprocessing techniques [31]. These techniques include tokenization, lemmatization, and stop-word removal, which ensure that the data are structured and relevant for analysis.  
* Tokenization: The input text is split into smaller units called tokens, often at the sentence or word level. For example, the sentence \"Links that should be clickable aren't underlined and aren't clickable\" is tokenized into individual words such as \"Links\", \"that\", \"should\", \"be\", \"clickable\", \"aren't\", \"underlined\", \"and\", \"aren't\", and \"clickable\".
* Lemmatization: The roots or base forms of the words (lemmas) are extracted from the tokens. For instance, words such as \"aren't\" and \"clickable\" are reduced to their base forms, \"are not\" and \"click\", respectively. Similarly, \"underlined\" becomes \"underline\". This step standardizes words to their root forms, enabling more effective feature matching and analysis.
* Stop-Word Removal: Commonly used words that do not contribute significantly to the semantic meaning of a sentence, such as \"should\" and \"be\", are removed. These words frequently appear in the text but carry minimal analytical value in the context of bug report classification.  
By preprocessing the data in this manner, the researchers ensured that the text used for analysis was concise, relevant, and semantically meaningful. This preprocessing step was crucial for improving the efficiency and accuracy of the subsequent feature extraction and machine learning processes.  
Figure 3: Overview of the proposed method.
## 3 Results and Discussion
### Type I Error Analysis
#### 3.1.1 Quality Control #1  
The data are considered to be unbiased, and intervals are defined around the zero value (signed errors are considered). This situation is ideal because it is desirable that no bias is present, and it is equivalent to taking the absolute values of errors. As a guide, the tolerances are established in relation to the Gaussian model. In this situation, it is mandatory to distinguish between 1D (infrastructure) and 3D (urban and natural) cases. The tolerance values are as follows:* For the 1D study case (infrastructure sample), since the errors were computed according to the Z discrepancies, the NMAD value is \(\pm 0.02\) m. The values are derived as follows: \(T_{1}=T_{50\%}=0.6745\times 0.020\) m and \(T_{2}=T_{90\%}=1.6449\times 0.020\) m
* For the two 3D study cases (urban and natural samples) where the error was computed according to a 3D signed distance, a unique reference value is used via the arithmetic mean of both the NMAD (\(\pm 0.088\) and \(\pm 0.064\) m), which is \(\pm 0.075\) m, and the values derived as follows: \(T_{1}=T_{50\%}=0.51\times(3\times 0.075)\) m and \(T_{2}=T_{90\%}=0.833\times(3\times 0.075)\) m Consequently, three categories of errors (CoE1..3) are defined as follows:
* CoE1: \(|E_{i}|\in[0,\ T_{1}]=[0,\ 0.0135]\) m.
- CoE2: \(|E_{i}|\in(T_{1};\ T_{2}]=(0.0135;\ 0.0329]\) m
- CoE3: \(|E_{i}|\in(T_{2},\ \infty)=(0.0329,\ \infty)\) m.
* CoE1: \(|E_{i}|\in[0,\ T_{1}]=[0,\ 0.1147\ ]\) m.
- CoE2: \(|E_{i}|\in(T_{1}\,\ T_{2}]=(0.1147,\ 0.1874]\) m
- CoE3: \(|E_{i}|\in T_{2}=(0.1874,\ \infty)\) m.  
The results obtained by the simulation following the process described at the beginning of this section are shown in Table 7. The situation is very clear, and the controls on the infrastructure data usually result in a rejection of the null hypothesis (i.e., the null hypothesis not accepted), even with small sample sizes. This finding implies that the proposed limits are very restrictive or, in other words, that the data do not verify the specifications. Otherwise, for the remaining two cases of study, the test always results in the acceptance of the null hypothesis. This finding implies that true limits of the errors are narrower than the specifications being considered.
# Backdoor Attack Against Dataset Distillation in Natural Language Processing
## 2 Related Work
### Backdoor Defense  
Based on the assumed threat models established by various defense strategies, current mechanisms for defending against backdoor attacks for textual data can be broadly categorized into three types.  
The first category includes defense techniques that operate under the assumption that the defender can access a portion of a clean trusted dataset. For instance, [PERSON] et al. [73] identified backdoors in training data prior to classification by a poisoned model by using outlier or anomaly detection methods. A perplexity score was introduced by [PERSON] et al. [74] to detect context-free backdoor trigger words during inference. Additionally, [PERSON] et al. [75] used the output probabilities from the compromised classifier as a signal for differentiating between poisoned and nonpoisoned input samples. Similarly, [PERSON] et al. [76] analyzed embeddings derived from the hidden layers of the poisoned model for both poisoned and nonpoisoned samples to create a metric for detecting backdoors. [PERSON] et al. [77] implemented an input replication technique that generates distinguishing metrics for contaminated and uncontaminated samples by replacing a percentage k of words with those from another class while calculating the Shannon entropy.  
In the second category [73; 78], defense mechanisms are further classified based on scenarios where the defenders do not have access to any trusted datasets, and instead must work with potentially compromised training datasets containing adversarially implanted triggers. The detection metric for backdoors in this case is developed through training on the tainted models and analyzing their hidden-layer activations.  
The third category operates under the assumption that there is neither access to the training dataset nor any white-box insight into the poisoned model; instead, it relies on black-box or query-based approaches to detect backdoor triggers [79]. This method identifies backdoors during inference phases, representing a passive form of detection, since it occurs after the model is compromised via contaminated training data. Thus, such detections happen significantly later than pretraining interventions.  
We have organized the main existing backdoor attack defense methods in Table 1.
## 3 RUL Prediction Methodology
### Envelope Analysis for Bearing Fault Detection
#### 3.1.2 Averaged Envelope Spectrum  
In general, the vibration acceleration signal of faulty bearings can be characterized as a series of impulsive components recurring at the fault characteristic frequencies (FCFs), wherein it can be modelled as the amplitude modulation on the resonance responses of bearing faults [26; 27]. Given a real-valued time-domain acceleration signal \(a(t)\), one way to extract the repetition components related to the bearing fault is to compute the envelope of its complex analytic signal, \(a_{c}(t)\),  
\[a_{c}(t)=a(t)+\widetilde{a}(t)j, \tag{1}\]  
where \(\widetilde{a}(t)\) denotes the Hilbert transform of \(a(t)\)[27] and \(j\) is a complex imaginary unit. The envelope signal is defined as the magnitude of the complex analytic signal, which is given by  
\[e(t)=\left|a(t)+\widetilde{a}(t)j\right|=\sqrt{a(t)^{2}+\widetilde{a}(t)^{2}}. \tag{2}\]  
After the high-pass filtering, rather than directly calculating the Fourier spectrum of the envelope signal, the operation of power spectral density (PSD) is performed based on multiple segments of the original envelope with a selected degree of overlapping. This process results in the averaged envelope spectrum (AES) which attempts to suppress the interference of spurious frequency peaks and random noise. As indicated by [PERSON] [27], due to the randomness in choosing the start point and length of the signal segments, by introducing a segmentation and averaging process, the averaged spectrum computed via PSD can be closer to the real frequency peaks within the signal. The averaged envelope spectrum \(S_{e}(f_{k})\) based on the PSD of the envelope signal \(e(t)\) can be written as  
\[S_{e}(f_{k})=E[S(f_{k})S^{*}(f_{k})]=\frac{\sum_{i=1}^{N_{s}}S_{i}(f_{k})S_{i}^ {*}(f_{k})}{N_{s}}, \tag{3}\]  
where \(N_{s}\) is the total number of segments of the envelope signal \(e(t)\), \(S_{i}(f_{k})\) denotes the Fourier transform of the \(i\)-th envelope segment at the frequency location \(f_{k}(0\leq f_{k}\leq f_{s}/2)\), and \(S_{i}^{*}(f_{k})\) is accordingly the complex conjugate of \(S_{i}(f_{k})\). \(k=0,\ 1,...,L/2-1\) denotes the index of the frequency bin for the \(L\)-point Fourier transform for each segment and \(f_{s}\) the sampling frequency.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 1 Introduction
Therefore, to achieve a fully automatic system for ship navigation in future, an automatic navigation system of a ship in the fairway is needed in combination with the above-mentioned systems, which is proposed in this study. This system, is illustrated as in Figure 1, performs three tasks: First, the ship's rudder is automatically controlled by a fuzzy logic controller to track the planned route; second, the ship's speed is decreased by the PID controller to approach the berth area at a low speed; finally, when the ship's rudder is not effective enough at a low speed to change the ship's heading to the desired angle, the ship heading is adjusted appropriately by the bow thruster before berthing maneuvering. The bow thruster is also regulated by a second fuzzy controller. To illustrate the effectiveness of this system, numerical simulations were conducted with training ship of Mokpo Maritime University. The results showed that the proposed system has the performance necessary for automatic ship navigation in the fairway area. Figure 1: Automatic navigation system of a ship in a fairway.
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Data Normalization  
To improve prediction accuracy, we normalized the values of the NO\({}_{2}\) concentration using min-max normalization, which was applied by scaling the data to avoid biasing the model towards features with larger values [45]. To this end, a minimum and maximum scale was used to measure and standardize the continuous data. The intention was to maintain the distribution of features during standardization. After scaling, all features were compressed to a scale from 0 to 1. The method of doing so is given in the following equation:  
\[x=\frac{x-min}{max-min} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**City** & **Air Pollutants** & **Unit** & **Year** & **\#NaN** & **Mean** & **Std** \\ \hline \multirow{8}{*}{Najran} & \multirow{2}{*}{CO} & \multirow{2}{*}{mg/m\({}^{3}\)} & 2022 & 6 & 27,128.34 & 3291.96 \\  & & & 2023 & 1 & 28,294.89 & 2891.70 \\ \cline{2-7}  & & & 2022 & 9 & 87.627703 & 62.14 \\  & & & 2023 & 6 & 95.129175 & 64.995 \\ \cline{2-7}  & & & 2022 & 4 & 88.835017 & 33.097 \\  & & & 2023 & 5 & 86.023103 & 28.09 \\ \cline{2-7}  & & & 2022 & 8 & 120,058.23 & 5867.48 \\  & & & 2023 & 5 & 118,408.20 & 6370.41 \\ \cline{2-7}  & & & 2022 & 8 & 83.807 & 244.70 \\  & & & 2023 & 5 & 111.05 & 248.75 \\ \hline \multirow{8}{*}{Al Riyadh} & \multirow{2}{*}{NO\({}_{2}\)} & \multirow{2}{*}{mg/m\({}^{3}\)} & 2022 & 4 & 31,550.50 & 3379.61 \\  & & & 2023 & 5 & 31,582.88 & 3038.82 \\ \cline{2-7}  & & & 2022 & 8 & 114.49 & 73.76 \\  & & & 2023 & 6 & 121.99 & 69.35 \\ \cline{2-7}  & & & 2022 & 4 & 263.29 & 169.86 \\  & & & 2023 & 5 & 265.56 & 165.34 \\ \cline{2-7}  & & & 2022 & 6 & 123,616.57 & 5957.60 \\  & & & 2023 & 5 & 121,498.27 & 5705.12 \\ \cline{2-7}  & & & 2022 & 7 & 129.04 & 262.78 \\  & & & 2023 & 5 & 130.72 & 309.46 \\ \hline \multirow{8}{*}{Al Jubail} & \multirow{2}{*}{CO} & \multirow{2}{*}{mg/m\({}^{3}\)} & 2022 & 4 & 33,643.41 & 3386.99 \\  & & & 2023 & 1 & 34,123.94 & 2920.209 \\ \cline{1-1} \cline{2-7}  & & & 2022 & 2 & 134.06 & 106.50 \\ \cline{1-1}  & & & 2023 & 13 & 156.81 & 114.71 \\ \cline{1-1} \cline{2-7}  & & & 2022 & 4 & 183.03 & 86.55 \\ \cline{1-1}  & & & 2023 & 6 & 184.17 & 85.23 \\ \cline{1-1} \cline{2-7}  & & & 2022 & 6 & 125,443.72 & 6258.49 \\ \cline{1-1}  & & & 2023 & 5 & 124,113.17 & 5297.34 \\ \cline{1-1} \cline{2-7}  & & & 2022 & 8 & 307.31 & 342.74 \\ \cline{1-1}  & & & 2023 & 12 & 334.10 & 393.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the air pollutants in the three target cities: (**a**) Najran; (**b**) Al Riyadh; and (**c**) Al Jubail.
# A Survey on Blockchain-Based Search Engines
## 3 Search Engines and Their Approaches
### Blockchain-Based Search Engine to Address the Problems  
Blockchain is a chain of blocks that store information securely in a decentralized and distributed network using cryptography techniques. Decentralization, immutability, transparency, and auditability are all features of blockchain that make it more secure and tamperproof. The following are four essential characteristics of blockchain, including decentralization, anonymity, persistency, and auditability.  
* Decentralization: Each transaction in a traditional centralized transaction system must be validated by a central trusted agency. This centralization results in cost and performance bottleneck and hence monopolization in the delegated agency decisions. A transaction in the blockchain network can be completed between any two peers (P2P) without central agency authentication. In this manner, blockchain can reduce the trust concern and prevent monopoly in any dimension of the trusted agency.
* Anonymity: With a created address, each user can communicate with the blockchain network. Furthermore, a user could generate a large number of lessons to protect their identity. There is no longer a central authority in charge of users' personal information. This approach ensures that the transactions in the blockchain are kept private to some extent.
* Persistency: It is nearly impossible to tamper with the network because each transaction must be validated and recorded in blocks distributed throughout the network. Other nodes would also validate each broadcasted block, and transactions would be examined. As a result, any falsification would be easily detectable.
* Auditability: A digital distributed ledger records all transactions in a blockchain network and verifies them with a digital timestamp. As a result, any node in the network can be accessed to audit and trace records.  
According to the characteristics of the blockchain, blockchain-based search engines, unlike their conventional counterparts, do not have centralized controls. Instead, they are distributed across multiple networks. By decentralization, the trust concerns are reduced, and centralized control's side effects, such as monopoly, are eliminated.  
Figure 3: The YaCy architecture [52].  
Additionally, considering anonymity, with a blockchain-based, no company can lay claims to users' data or violate user privacy. The persistency makes it impossible to tamper with the data because all data will be validated, encrypted, and stored on a network of blocks. Blockchain-based search ecosystems can further provide users with a private key to protect their data.
## 4 Classification Effect on the Dataset
### Classification Effect on Binary Classification Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Rank** & **Model Type** & **Accuracy (\%)** & **Total Cost** & **Preset** & **Prediction Speed (obs/s)** & **Training Time (s)** \\ \hline
1 & Ensemble & 97.6 & 12 & Custom Ensemble & 274.21 & 168.61 \\
2 & SVM & 97.4 & 13 & Custom SVM & 20,898.55 & 41.13 \\
3 & Ensemble & 97.4 & 13 & Bagged Trees & 2703.39 & 3.53 \\
4 & Neural Network & 97.2 & 14 & Custom Neural Network & 17,686.34 & 165.26 \\
5 & Tree & 97.2 & 14 & Custom Tree & 13,993.02 & 17.42 \\
6 & SVM & 97.2 & 14 & Cubic SVM & 14,767.65 & 1.11 \\
7 & Kernel & 97.2 & 14 & SVM Kernel & 17,235.55 & 1.84 \\
8 & Neural Network & 97.0 & 15 & Trilayered Neural Network & 17,110.86 & 1.03 \\
9 & Tree & 96.8 & 16 & Fine Tree & 22,969.39 & 1.72 \\
10 & Tree & 96.8 & 16 & Medium Tree & 21,404.11 & 3.76 \\
11 & SVM & 96.8 & 16 & Medium Gaussian SVM & 12,757.80 & 2.04 \\
12 & Ensemble & 96.8 & 16 & RUSBoosted Trees & 6180.08 & 2.12 \\
13 & Ensemble & 96.6 & 17 & Subspace KNN & 924.60 & 3.72 \\
14 & Neural Network & 96.6 & 17 & Medium Neural Network & 21,523.24 & 4.87 \\
15 & SVM & 96.4 & 18 & Quadratic SVM & 15,984.76 & 1.40 \\
16 & KNN & 96.2 & 19 & Custom KNN & 4506.50 & 18.77 \\
17 & KNN & 96.2 & 19 & Fine KNN & 7484.53 & 3.06 \\
18 & Neural Network & 96.2 & 19 & Narrow Neural Network & 16,441.53 & 1.68 \\
19 & Neural Network & 96.2 & 19 & Wide Neural Network & 22,565.42 & 1.03 \\
20 & Neural Network & 96.2 & 19 & Bilayered Neural Network & 20,544.01 & 1.62 \\
21 & KNN & 96.0 & 20 & Weighted KNN & 8537.68 & 2.07 \\
22 & Kernel & 96.0 & 20 & Logistic Regression Kernel & 16,205.77 & 1.09 \\
23 & Tree & 95.8 & 21 & Coarse Tree & 16,049.92 & 2.09 \\
24 & SVM & 95.8 & 21 & Linear SVM & 12,219.59 & 2.11 \\
25 & Logistic Regression & 95.4 & 23 & Logistic Regression & 4623.44 & 3.51 \\
26 & KNN & 95.2 & 24 & Medium KNN & 7554.55 & 1.69 \\
27 & Naive Bayes & 94.8 & 26 & Custom Naive Bayes & 2382.31 & 63.52 \\
28 & Naive Bayes & 94.8 & 26 & Kernel Naive Bayes & 1967.08 & 3.54 \\
29 & KNN & 94.8 & 26 & Cosine KNN & 7863.22 & 0.83 \\
30 & SVM & 94.6 & 27 & Fine Gaussian SVM & 12,950.65 & 0.79 \\
31 & KNN & 94.6 & 27 & Cubic KNN & 5804.98 & 0.95 \\
32 & Discriminant & 92.4 & 38 & Custom Optimizable Discriminant & 9477.07 & 28.42 \\
33 & Naive Bayes & 92.4 & 38 & Gaussian Naive Bayes & 4518.48 & 3.67 \\
34 & SVM & 91.2 & 44 & Coarse Gaussian SVM & 14,743.48 & 1.92 \\
35 & Ensemble & 90.6 & 47 & Subspace Discriminant & 1371.42 & 3.28 \\
36 & Discriminant & 89.4 & 53 & Linear Discriminant & 12721.64 & 1.16 \\
37 & KNN & 86.6 & 67 & Coarse KNN & 7719.47 & 2.57 \\
38 & Ensemble & 60.0 & 200 & Boosted Trees & 19,922.38 & 2.83 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of different classifiers on binary classification tasks.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 3 Algorithmic Principle
### FG Optimization Positioning Method Assisted by SFLA
#### 3.4.2 BDAPS Factor Graph Framework  
In the above process, we set up the corresponding measurement model and process model for the observation domain and the location domain respectively. It can be found that the factor nodes of the observation domain and the location domain are associated with the current time variable node and the previous time variable node. The state variables of BDAPS system are parameters concerning distance measurements and user position coordinates. Let the combined state quantity be as follows:  
\[A_{k}=[\rho s_{1},\rho s_{2},\ldots,\rho s_{N},x_{k},y_{k},z_{k}] \tag{35}\]  
According to the construction process in the previous section, all of the measurement information obtained by farmers in the navigation system is integrated to build the measurement set \(Z_{k}\). The Maximum a posteriori (MAP) estimation density estimation is used to find the maximum a posteriori value of the navigation state set \(A_{k}\) for information fusion. Then the cooperative navigation problem is transformed into an equivalent nonlinear optimization problem.  
\[\begin{array}{ll}\widetilde{A}_{k}&=\underset{A_{k}}{\arg\min}[-\ln P(A_{k} |Z_{k})]\\ &=\underset{A_{k}}{\arg\min}[-\ln(\prod\limits_{i=u}^{k}P(x_{i}\big{|}x_{i-1}, \kappa_{i}^{pro})\prod\limits_{i=u}^{k}P(\kappa_{i}^{meas}\big{|}x_{i}))]\\ &=\underset{A_{k}}{\arg\min}[\sum\limits_{i=u}^{u}\|f_{all,i}^{pro}(x_{i-1}, \kappa_{i}^{pro})-x_{i}\|_{H_{i}}^{2}+\sum\limits_{i=u}^{u}\|f_{all,i}^{meas}(x _{i})-\kappa_{i}^{meas})\|_{l_{i}}^{2}]\end{array} \tag{36}\]  
I\({}_{i}\) is the measurement noise covariance, H\({}_{i}\) is the process noise covariance, \(f_{all,i}^{meas}(x_{i})\) is the nonlinear measurement function of the measurement model, \(f_{all,i}^{pro}(x_{i-1},\kappa_{i}^{pro})\) is the nonlinear state transfer function of the process model, \(\|\|^{2}\) is the square Markov distance. The estimation process of the state vector becomes the problem of finding the minimum point of the nonlinear function. The factorial optimization model architecture for BDAPS is shown in Figure 3.
## 5 Processing Data in the Encrypted Domain: Secure Data Aggregation
Homomorphic encryption can be used to avoid the need to decrypt the information that must be aggregated and encrypt the result and operate on the encrypted data directly. This can dramatically increase the performance, in terms of execution time and energy savings, since the encryption/decryption operations are typically computationally demanding. Besides computational and energy efficiencies, a positive side effect of the adoption of homomorphic encryption is the fact that only the sources and the final destination of the data are capable of accessing the real data, thus preserving \"end-to-end\" confidentiality for the aggregation  
Figure 4: In-network data aggregation. application scenario. Additional security can be introduced by using _probabilistic_ cryptosystems, such as the Pailler cryptosystem: in this case, given two encrypted values, it is not possible to decide whether they conceal the same value or not. This is especially useful to avoid that eavesdroppers can determine the content of a secure communication just by observing the encrypted packets.