diff --git a/apt/risk/data_assessment/__init__.py b/apt/risk/data_assessment/__init__.py
new file mode 100644
index 0000000..330fc94
--- /dev/null
+++ b/apt/risk/data_assessment/__init__.py
@@ -0,0 +1,9 @@
+"""
+Module providing privacy risk assessment for synthetic data.
+
+The main interface, ``DatasetAttack``, with the assess_privacy() main method assumes the availability of the
+training data, holdout data and synthetic data at the time of the privacy evaluation.
+It is implemented by two types of abstract classes: ``DatasetAttackPerRecord`` and ``DatasetAttackWhole``, to be
+implemented by concrete assessment methods.
+"""
+from apt.risk.data_assessment import dataset_attack
diff --git a/apt/risk/data_assessment/attack_strategy_utils.py b/apt/risk/data_assessment/attack_strategy_utils.py
new file mode 100644
index 0000000..c08658f
--- /dev/null
+++ b/apt/risk/data_assessment/attack_strategy_utils.py
@@ -0,0 +1,72 @@
+import abc
+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+from tqdm import tqdm
+
+from apt.utils.datasets import ArrayDataset
+
+
+class AttackStrategyUtils(abc.ABC):
+    """
+        Abstract base class for common utilities of various privacy attack strategies.
+    """
+    ...
+
+
+class KNNAttackStrategyUtils(AttackStrategyUtils):
+    """
+         Common utilities for attack strategy based on KNN distances.
+    """
+
+    def __init__(self, k: int, use_batches: bool = False, batch_size: int = 0) -> None:
+        """
+        :param k: How many nearest neighbors to search
+        :param use_batches: Use batches with a progress meter or not when finding KNNs for query set
+        :param batch_size: if use_batches=True, the size of batch_size should be > 0
+        """
+        self.k = k
+        self.use_batches = use_batches
+        self.batch_size = batch_size
+        if use_batches:
+            if batch_size < 1:
+                raise ValueError(f"When using batching batch_size should be > 0, and not {batch_size}")
+
+    def fit(self, dataset: ArrayDataset, knn_learner: NearestNeighbors):
+        knn_learner.fit(dataset.get_samples())
+
+    def find_knn(self, query_samples: ArrayDataset, knn_learner: NearestNeighbors, distance_processor=None):
+        """
+        Main nearest neighbor search function on synthetic data.
+        :param query_samples: query samples
+        :param knn_learner: unsupervised learner for implementing neighbor searches
+        :param distance_processor: function for processing the distance into another more relevant metric per sample.
+            Its input is an array representing distances (the distances returned by NearestNeighbors.kneighbors() ),
+            and the output should be another array with distance-based values that enable to compute the final score
+        :return:
+            distances of the query samples to their nearest neighbors, or a metric based on that distance and calculated
+            by the distance_processor function
+        """
+        samples = query_samples.get_samples()
+        if not self.use_batches:
+            distances, _ = knn_learner.kneighbors(samples, self.k, return_distance=True)
+            if distance_processor:
+                return distance_processor(distances)
+            else:
+                return distances
+
+        probabilities = []
+        for i in tqdm(range(len(samples) // self.batch_size)):
+            x_batch = samples[i * self.batch_size:(i + 1) * self.batch_size]
+            x_batch = np.reshape(x_batch, [self.batch_size, -1])
+
+            # dist_batch: distance between every query sample in batch to its KNNs among training samples
+            dist_batch, _ = knn_learner.kneighbors(x_batch, self.k, return_distance=True)
+
+            # The probability of each sample to be generated
+            if distance_processor:
+                probability_per_sample_batch = distance_processor(dist_batch)
+                probabilities.append(probability_per_sample_batch)
+            else:
+                probabilities.append(dist_batch)
+        return np.concatenate(probabilities)
diff --git a/apt/risk/data_assessment/dataset_assessment_manager.py b/apt/risk/data_assessment/dataset_assessment_manager.py
new file mode 100644
index 0000000..41b9d6b
--- /dev/null
+++ b/apt/risk/data_assessment/dataset_assessment_manager.py
@@ -0,0 +1,65 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import pandas as pd
+
+from apt.risk.data_assessment.dataset_attack_gan_leaks import DatasetAttackGanLeaksConfig, DatasetAttackGanLeaks, \
+    DatasetAttackScoreGanLeaks
+from apt.risk.data_assessment.dataset_attack_holdout import DatasetAttackHoldoutConfig, DatasetAttackHoldout, \
+    DatasetAttackScoreHoldout
+from apt.utils.datasets import ArrayDataset
+
+
+@dataclass
+class DatasetAssessmentManagerConfig:
+    persist_reports: bool = True
+    generate_plots: bool = False
+
+
+class DatasetAssessmentManager:
+    """
+    The main class for running dataset assessment attacks.
+    """
+    gan_leaks_attack_scores = []
+    holdout_attack_scores = []
+
+    def __init__(self, config: Optional[DatasetAssessmentManagerConfig] = DatasetAssessmentManagerConfig) -> None:
+        """
+        :param config: Configuration parameters to guide the assessment process such as which attack
+               frameworks to use, optional
+        """
+        self.config = config
+
+    def assess(self, original_data_members: ArrayDataset, original_data_non_members: ArrayDataset,
+               synthetic_data: ArrayDataset, dataset_name: str) -> (
+            DatasetAttackScoreGanLeaks, DatasetAttackScoreHoldout):
+        config_gl = DatasetAttackGanLeaksConfig(use_batches=False)
+        mgr = DatasetAttackGanLeaks(original_data_members,
+                                    original_data_non_members,
+                                    synthetic_data,
+                                    dataset_name,
+                                    config_gl)
+
+        result = mgr.assess_privacy()
+        score_g = mgr.calculate_privacy_score(result, generate_plot=self.config.generate_plots)
+        self.gan_leaks_attack_scores.append(score_g)
+
+        config_h = DatasetAttackHoldoutConfig(use_batches=False)
+        mgr_h = DatasetAttackHoldout(original_data_members, original_data_non_members, synthetic_data,
+                                     dataset_name,
+                                     config_h)
+
+        score_h = mgr_h.assess_privacy()
+        self.holdout_attack_scores.append(score_h)
+        return score_g, score_h
+
+    def dump_all_scores_to_files(self):
+        if self.config.generate_plots:
+            results_log_file = "_results.log.csv"
+            self.dump_scores_to_file(self.gan_leaks_attack_scores, "gan_leaks" + results_log_file, True)
+            self.dump_scores_to_file(self.holdout_attack_scores, "holdout" + results_log_file, True)
+
+    @staticmethod
+    def dump_scores_to_file(attack_scores, filename, header: bool):
+        run_results_df = pd.DataFrame(attack_scores)
+        run_results_df.to_csv(filename, header=header, encoding='utf-8', index=False, mode='w')  # Overwrite
diff --git a/apt/risk/data_assessment/dataset_attack.py b/apt/risk/data_assessment/dataset_attack.py
new file mode 100644
index 0000000..e2184c7
--- /dev/null
+++ b/apt/risk/data_assessment/dataset_attack.py
@@ -0,0 +1,133 @@
+"""
+This module defines the interface for privacy risk assessment of synthetic datasets.
+"""
+import abc
+from typing import Optional, Union
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import metrics
+from sklearn.metrics import RocCurveDisplay
+
+from apt.risk.data_assessment.attack_strategy_utils import AttackStrategyUtils
+from apt.risk.data_assessment.dataset_attack_result import DatasetAttackScore, DatasetAttackResultPerRecord, \
+    DatasetAttackResult
+from apt.utils.datasets import ArrayDataset
+
+
+class Config:
+    """
+        The base class for dataset attack configurations
+    """
+    ...
+
+
+class DatasetAttack(abc.ABC):
+    """
+         The interface for performing privacy risk assessment for synthetic datasets.
+    """
+
+    def __init__(self, original_data_members: ArrayDataset, original_data_non_members: ArrayDataset,
+                 synthetic_data: ArrayDataset, dataset_name: str, attack_strategy_utils: AttackStrategyUtils,
+                 config: Optional[Config] = Config()) -> None:
+        """
+        :param original_data_members: A container for the training original samples and labels
+        :param original_data_non_members: A container for the holdout original samples and labels
+        :param synthetic_data: A container for the synthetic samples and labels
+        :param dataset_name: A name to identify the dataset under attack
+        :param attack_strategy_utils: Utils for use with the attack strategy
+        :param config: Configuration parameters to guide the assessment process such as which attack
+               frameworks to use, optional
+        """
+
+        self.original_data_members = original_data_members
+        self.original_data_non_members = original_data_non_members
+        self.synthetic_data = synthetic_data
+        self.dataset_name = dataset_name
+        self.attack_strategy_utils = attack_strategy_utils
+        self.config = config
+
+    @abc.abstractmethod
+    def assess_privacy(self) -> Union[DatasetAttackScore, DatasetAttackResult]:
+        """
+        Assess the privacy of the dataset
+        :return:
+            result: Union[DatasetAttackScore, DatasetAssessmentResult] can be either the final privacy attack score,
+            or an intermediate attack result, which can be translated into a privacy score if needed
+        """
+        ...
+
+
+class DatasetAttackPerRecord(DatasetAttack):
+    """
+         An abstract base class for performing privacy risk assessment for synthetic datasets on a per-record level.
+    """
+
+    @abc.abstractmethod
+    def assess_privacy(self) -> DatasetAttackResultPerRecord:
+        """
+        Assess the privacy of the dataset
+        :return:
+            result: DatasetAssessmentResult
+        """
+        ...
+
+    @abc.abstractmethod
+    def calculate_privacy_score(self, dataset_attack_result: DatasetAttackResultPerRecord,
+                                generate_plot=False) -> DatasetAttackScore:
+        """
+        Calculate dataset privacy score based on the result of the privacy assessment
+        :return:
+            result: DatasetAttackScore
+        """
+        ...
+
+    def plot_roc_curve(self, pos_probabilities, neg_probabilities, name_prefix=""):
+        """
+        Plot ROC curve
+        :param pos_probabilities: loss of the positive samples, the training data
+        :param neg_probabilities: loss of the negative samples, the hold-out data
+        :param name_prefix: name prefix for the ROC curve plot
+        """
+        labels = np.concatenate((np.zeros((len(neg_probabilities),)), np.ones((len(pos_probabilities),))))
+        results = np.concatenate((neg_probabilities, pos_probabilities))
+        svc_disp = RocCurveDisplay.from_predictions(labels, results)
+        svc_disp.plot()
+        plt.plot([0, 1], [0, 1], color="navy", linewidth=2, linestyle="--", label='No skills')
+        plt.title('ROC curve')
+        plt.savefig(f'{name_prefix}{self.dataset_name}_roc_curve.png')
+
+    @staticmethod
+    def calculate_roc_score(pos_probabilities, neg_probabilities):
+        """
+        Plot ROC curve
+        :param pos_probabilities: probability estimates of the positive samples, the training data
+        :param neg_probabilities: probability estimates of the negative samples, the hold-out data
+        :return:
+            fpr: False Positive rate
+            tpr: True Positive rate
+            threshold: threshold
+            auc: area under the Receiver Operating Characteristic Curve
+            ap: average precision score
+        """
+        labels = np.concatenate((np.zeros((len(neg_probabilities),)), np.ones((len(pos_probabilities),))))
+        results = np.concatenate((neg_probabilities, pos_probabilities))
+        fpr, tpr, threshold = metrics.roc_curve(labels, results, pos_label=1)
+        auc = metrics.roc_auc_score(labels, results)
+        ap = metrics.average_precision_score(labels, results)
+        return fpr, tpr, threshold, auc, ap
+
+
+class DatasetAttackWhole(DatasetAttack):
+    """
+         An abstract base class for performing privacy risk assessment for synthetic datasets on a whole-dataset level.
+    """
+
+    @abc.abstractmethod
+    def assess_privacy(self) -> DatasetAttackScore:
+        """
+        Assess the privacy of the dataset
+        :return:
+            result: DatasetAssessmentResult
+        """
+        ...
diff --git a/apt/risk/data_assessment/dataset_attack_gan_leaks.py b/apt/risk/data_assessment/dataset_attack_gan_leaks.py
new file mode 100644
index 0000000..a8ee00c
--- /dev/null
+++ b/apt/risk/data_assessment/dataset_attack_gan_leaks.py
@@ -0,0 +1,127 @@
+"""
+This module implements privacy risk assessment of synthetic datasets based on the paper:
+"GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" by D. Chen, N. Yu, Y. Zhang, M. Fritz
+published in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 343–62, 2020.
+https://doi.org/10.1145/3372297.3417238 and its implementation in https://github.com/DingfanChen/GAN-Leaks.
+"""
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+
+from apt.risk.data_assessment.attack_strategy_utils import KNNAttackStrategyUtils
+from apt.risk.data_assessment.dataset_attack import DatasetAttackPerRecord, Config
+from apt.risk.data_assessment.dataset_attack_result import DatasetAttackScore, DatasetAttackResultPerRecord
+from apt.utils.datasets import ArrayDataset
+
+
+@dataclass
+class DatasetAttackGanLeaksConfig(Config):
+    """Configuration for DatasetAttackGanLeaks.
+
+    Attributes:
+        k: Number of nearest neighbors to search
+        use_batches: Divide query samples into batches or not.
+        batch_size:  Query sample batch size.
+        compute_distance: A callable function, which takes two arrays representing 1D vectors as inputs and must return
+            one value indicating the distance between those vectors. See sklearn.neighbors.NearestNeighbors documentation.
+        batch_size:  Additional keyword arguments for the distance computation function.
+    """
+    k: int = 1
+    use_batches: bool = False
+    batch_size: int = 10
+    compute_distance: callable = None
+    distance_params: dict = None
+
+
+@dataclass
+class DatasetAttackScoreGanLeaks(DatasetAttackScore):
+    """Configuration for DatasetAttackGanLeaks.
+    Attributes
+    ----------
+    roc_auc_score : the share of synthetic records closer to the training than the holdout dataset
+    average_precision_score:
+    assessment_type : assessment type is 'GANLeaks', to be used in reports
+    """
+    roc_auc_score: float
+    average_precision_score: float
+    assessment_type: str = 'GANLeaks'
+
+
+class DatasetAttackGanLeaks(DatasetAttackPerRecord):
+    """
+         Privacy risk assessment for synthetic datasets based Black-Box MIA attack using distances of
+         members (training set) and non-members (holdout set) from their nearest neighbors in the synthetic dataset.
+         The area under the receiver operating characteristic curve (AUCROC) gives the privacy risk measure.
+    """
+
+    def __init__(self, original_data_members: ArrayDataset, original_data_non_members: ArrayDataset,
+                 synthetic_data: ArrayDataset, dataset_name: str,
+                 config: Optional[DatasetAttackGanLeaksConfig] = DatasetAttackGanLeaksConfig()):
+        """
+        :param original_data_members: A container for the training original samples and labels
+        :param original_data_non_members: A container for the holdout original samples and labels
+        :param synthetic_data: A container for the synthetic samples and labels
+        :param dataset_name: A name to identify this dataset
+        :param config: Configuration parameters to guide the assessment process such as which attack
+               frameworks to use, optional
+        """
+        attack_strategy_utils = KNNAttackStrategyUtils(config.k, config.use_batches, config.batch_size)
+        super().__init__(original_data_members, original_data_non_members, synthetic_data, dataset_name,
+                         attack_strategy_utils, config)
+        if config.compute_distance:
+            self.nn_obj = NearestNeighbors(n_neighbors=config.k, algorithm='auto', metric=config.compute_distance,
+                                           metric_params=config.distance_params)
+        else:
+            self.nn_obj = NearestNeighbors(n_neighbors=config.k, algorithm='auto')
+
+    def assess_privacy(self) -> DatasetAttackResultPerRecord:
+        """
+        Calculate probabilities of positive and negative samples to be generated by the synthetic data generator
+        :return:
+            :result of the attack, based on the NN distances from the query samples to the synthetic data samples
+        """
+        # nearest neighbor search
+        self.attack_strategy_utils.fit(self.synthetic_data, self.nn_obj)
+
+        # positive query
+        pos_proba = self.attack_strategy_utils.find_knn(self.original_data_members, self.nn_obj,
+                                                        self.probability_per_sample)
+
+        # negative query
+        neg_proba = self.attack_strategy_utils.find_knn(self.original_data_non_members, self.nn_obj,
+                                                        self.probability_per_sample)
+
+        result = DatasetAttackResultPerRecord(self.dataset_name, positive_probabilities=pos_proba,
+                                              negative_probabilities=neg_proba)
+        return result
+
+    def calculate_privacy_score(self, dataset_attack_result: DatasetAttackResultPerRecord,
+                                generate_plot=False) -> DatasetAttackScore:
+        """
+        Calculate probabilities of positive and negative samples to be generated by the synthetic data generator
+        :param dataset_attack_result attack result containing probabilities of positive and negative samples to be
+                generated by the synthetic data generator
+        :param generate_plot generate AUC ROC curve plot and persist it
+        :return:
+            :score of the attack, based on distance-based probabilities
+        """
+        pos_proba, neg_proba = \
+            dataset_attack_result.positive_probabilities, dataset_attack_result.negative_probabilities
+        fpr, tpr, threshold, auc, ap = self.calculate_roc_score(pos_proba, neg_proba)
+        score = DatasetAttackScoreGanLeaks(self.dataset_name, roc_auc_score=auc, average_precision_score=ap)
+        if generate_plot:
+            self.plot_roc_curve(pos_proba, neg_proba)
+        return score
+
+    @staticmethod
+    def probability_per_sample(distances: np.ndarray):
+        """
+        For every sample represented by its distance from the query sample to its KNN in synthetic data,
+        the probability of the synthetic data to be part of the query dataset.
+        :param distances: distance between every query sample in batch to its KNNs among synthetic samples
+        :return:
+            distances: probability estimates of the query samples being generated and so being part of the synthetic set
+        """
+        return np.average(np.exp(-distances), axis=1)
diff --git a/apt/risk/data_assessment/dataset_attack_holdout.py b/apt/risk/data_assessment/dataset_attack_holdout.py
new file mode 100644
index 0000000..216899e
--- /dev/null
+++ b/apt/risk/data_assessment/dataset_attack_holdout.py
@@ -0,0 +1,115 @@
+"""
+This module implements privacy risk assessment of synthetic datasets based on the paper
+"Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data" by M. Platzer and T. Reutterer.
+and on a variation of its reference implementation in https://github.com/mostly-ai/paper-fidelity-accuracy.
+"""
+import logging
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+
+from apt.risk.data_assessment.attack_strategy_utils import KNNAttackStrategyUtils
+from apt.risk.data_assessment.dataset_attack import DatasetAttackWhole, Config
+from apt.risk.data_assessment.dataset_attack_result import DatasetAttackScore
+from apt.utils.datasets import ArrayDataset
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DatasetAttackHoldoutConfig(Config):
+    """Configuration for DatasetAttackHoldout.
+
+    Attributes:
+        k: Number of nearest neighbors to search
+        use_batches:  Divide query samples into batches or not.
+        batch_size:   Query sample batch size.
+        compute_distance: A callable function, which takes two arrays representing 1D vectors as inputs and must return
+            one value indicating the distance between those vectors.
+        batch_size:   Additional keyword arguments for the distance computation function.
+    """
+    k: int = 1
+    use_batches: bool = False
+    batch_size: int = 10
+    compute_distance: callable = None
+    distance_params: dict = None
+
+
+@dataclass
+class DatasetAttackScoreHoldout(DatasetAttackScore):
+    """Configuration for DatasetAttackHoldout.
+    Attributes
+    ----------
+    share : the share of synthetic records closer to the training than the holdout dataset
+    assessment_type : assessment type is 'Holdout', to be used in reports
+    """
+    share: float
+    assessment_type: str = 'Holdout'
+
+
+class DatasetAttackHoldout(DatasetAttackWhole):
+    """
+         Privacy risk assessment for synthetic datasets based on distances of synthetic data records from
+         members (training set) and non-members (holdout set). The privacy risk measure is the share of synthetic
+         records closer to the training than the holdout dataset.
+    """
+
+    def __init__(self, original_data_members: ArrayDataset, original_data_non_members: ArrayDataset,
+                 synthetic_data: ArrayDataset, dataset_name: str,
+                 config: Optional[DatasetAttackHoldoutConfig] = DatasetAttackHoldoutConfig()):
+        """
+        :param original_data_members: A container for the training original samples and labels
+        :param original_data_non_members: A container for the holdout original samples and labels
+        :param synthetic_data: A container for the synthetic samples and labels
+        :param dataset_name: A name to identify this dataset
+        :param config: Configuration parameters to guide the assessment process such as which attack
+               frameworks to use, optional
+        """
+        attack_strategy_utils = KNNAttackStrategyUtils(config.k, config.use_batches, config.batch_size)
+        super().__init__(original_data_members, original_data_non_members, synthetic_data, dataset_name,
+                         attack_strategy_utils, config)
+        if config.compute_distance:
+            self.nn_obj_members = NearestNeighbors(n_neighbors=config.k, algorithm='auto',
+                                                   metric=config.compute_distance,
+                                                   metric_params=config.distance_params)
+            self.nn_obj_non_members = NearestNeighbors(n_neighbors=config.k, algorithm='auto',
+                                                       metric=config.compute_distance,
+                                                       metric_params=config.distance_params)
+        else:
+            self.nn_obj_members = NearestNeighbors(n_neighbors=config.k, algorithm='auto')
+            self.nn_obj_non_members = NearestNeighbors(n_neighbors=config.k, algorithm='auto')
+
+    def assess_privacy(self) -> DatasetAttackScoreHoldout:
+        """
+        Calculate the share of synthetic records closer to the training than the holdout dataset
+        :return:
+            :result of the attack, based on the NN distances from the query samples to the synthetic data samples
+        """
+        member_distances, non_member_distances = self.calculate_distances()
+        n_members = len(member_distances)
+        n_non_members = len(non_member_distances)
+        assert (n_members == n_non_members)
+        share = np.mean(member_distances < non_member_distances) + (n_members / (n_members + n_non_members)) * np.mean(
+            member_distances == non_member_distances)
+        score = DatasetAttackScoreHoldout(self.dataset_name, share=share)
+        return score
+
+    def calculate_distances(self):
+        """
+        Calculate positive and negative query probabilities, based on their distance to their KNNs among
+        synthetic samples.
+        :return:
+            pos_distances: distances of each synthetic data member from its nearest training samples
+            neg_distances: distances of each synthetic data member from its nearest validation samples
+        """
+        # nearest neighbor search
+        self.attack_strategy_utils.fit(self.original_data_members, self.nn_obj_members)
+        self.attack_strategy_utils.fit(self.original_data_non_members, self.nn_obj_non_members)
+
+        # distances of the synthetic data from the positive and negative samples (members and non-members)
+        pos_distances = self.attack_strategy_utils.find_knn(self.synthetic_data, self.nn_obj_members)
+        neg_distances = self.attack_strategy_utils.find_knn(self.synthetic_data, self.nn_obj_non_members)
+
+        return pos_distances, neg_distances
diff --git a/apt/risk/data_assessment/dataset_attack_result.py b/apt/risk/data_assessment/dataset_attack_result.py
new file mode 100644
index 0000000..d64d040
--- /dev/null
+++ b/apt/risk/data_assessment/dataset_attack_result.py
@@ -0,0 +1,19 @@
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass
+class DatasetAttackResult:
+    dataset_name: str
+
+
+@dataclass
+class DatasetAttackResultPerRecord(DatasetAttackResult):
+    positive_probabilities: np.ndarray
+    negative_probabilities: np.ndarray
+
+
+@dataclass
+class DatasetAttackScore:
+    dataset_name: str
diff --git a/requirements.txt b/requirements.txt
index fc006d1..39dda2e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,6 +3,7 @@ pandas~=1.1.0
 scipy==1.4.1
 scikit-learn>=0.22.2
 torch>=1.8.0
+tqdm>=4.64.1
 adversarial-robustness-toolbox>=1.11.0
 
 # testing
diff --git a/tests/test_data_assessment.py b/tests/test_data_assessment.py
new file mode 100644
index 0000000..96c4615
--- /dev/null
+++ b/tests/test_data_assessment.py
@@ -0,0 +1,175 @@
+import numpy as np
+import pytest
+from sklearn.compose import ColumnTransformer
+from sklearn.decomposition import PCA
+from sklearn.impute import SimpleImputer
+from sklearn.model_selection import GridSearchCV
+from sklearn.neighbors import KernelDensity
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder
+
+from apt.anonymization import Anonymize
+from apt.risk.data_assessment.dataset_assessment_manager import DatasetAssessmentManager, DatasetAssessmentManagerConfig
+from apt.utils.dataset_utils import get_iris_dataset_np, get_diabetes_dataset_np, get_adult_dataset_pd, \
+    get_nursery_dataset_pd
+from apt.utils.datasets import ArrayDataset
+
+NUM_SYNTH_SAMPLES = 40000
+NUM_SYNTH_COMPONENTS = 4
+
+iris_dataset_np = get_iris_dataset_np()
+diabetes_dataset_np = get_diabetes_dataset_np()
+nursery_dataset_pd = get_nursery_dataset_pd()
+adult_dataset_pd = get_adult_dataset_pd()
+
+mgr = DatasetAssessmentManager(DatasetAssessmentManagerConfig(persist_reports=True, generate_plots=False))
+
+
+def teardown_function():
+    mgr.dump_all_scores_to_files()
+
+
+anon_testdata = [('iris_np', iris_dataset_np, 'np', k, mgr) for k in range(2, 10, 4)] \
+                + [('diabetes_np', diabetes_dataset_np, 'np', k, mgr) for k in range(2, 10, 4)] \
+                + [('nursery_pd', nursery_dataset_pd, 'pd', k, mgr) for k in range(2, 10, 4)] \
+                + [('adult_pd', adult_dataset_pd, 'pd', k, mgr) for k in range(2, 10, 4)]
+
+
+@pytest.mark.parametrize("name, data, dataset_type, k, mgr", anon_testdata)
+def test_risk_anonymization(name, data, dataset_type, k, mgr):
+    (x_train, y_train), (x_test, y_test) = data
+
+    if dataset_type == 'np':
+        original_data_members = ArrayDataset(x_train, y_train)
+        QI = [0, 2]
+        anonymizer = Anonymize(k, QI, train_only_QI=True)
+        anonymized_data = ArrayDataset(anonymizer.anonymize(original_data_members))
+        original_data_non_members = ArrayDataset(x_test, y_test)
+    elif "adult" in name:
+        encoded, encoded_test = preprocess_adult_x_data(x_train, x_test)
+        QI = list(range(15, 27))
+        anonymizer = Anonymize(k, QI)
+        anonymized_data = ArrayDataset(anonymizer.anonymize(ArrayDataset(encoded, y_train)))
+        original_data_members = ArrayDataset(encoded, y_train)
+        original_data_non_members = ArrayDataset(encoded_test, y_test)
+    elif "nursery" in name:
+        encoded, encoded_test = preprocess_nursery_x_data(x_train, x_test)
+        QI = list(range(15, 27))
+        anonymizer = Anonymize(k, QI, train_only_QI=True)
+        anonymized_data = ArrayDataset(anonymizer.anonymize(ArrayDataset(encoded, y_train)))
+        original_data_members = ArrayDataset(encoded, y_train)
+        original_data_non_members = ArrayDataset(encoded_test, y_test)
+    else:
+        raise ValueError('Pandas dataset missing a preprocessing step')
+
+    score_g, score_h = mgr.assess(original_data_members, original_data_non_members, anonymized_data,
+                                  f'anon_k{k}_{name}')
+    assert (score_g.roc_auc_score > 0.5)
+    assert (score_g.average_precision_score > 0.5)
+
+    assert (score_h.share > 0.5)
+
+
+testdata = [('iris_np', iris_dataset_np, 'np', mgr),
+            ('diabetes_np', diabetes_dataset_np, 'np', mgr),
+            ('nursery_pd', nursery_dataset_pd, 'pd', mgr),
+            ('adult_pd', adult_dataset_pd, 'pd', mgr)]
+
+
+@pytest.mark.parametrize("name, data, dataset_type, mgr", testdata)
+def test_risk_kde(name, data, dataset_type, mgr):
+    (x_train, y_train), (x_test, y_test) = data
+
+    original_data_members = ArrayDataset(x_train, y_train)
+    original_data_non_members = ArrayDataset(x_test, y_test)
+
+    if dataset_type == 'np':
+        synth_data = ArrayDataset(kde(NUM_SYNTH_SAMPLES, n_components=NUM_SYNTH_COMPONENTS,
+                                      original_data=original_data_members.get_samples()))
+    elif "adult" in name:
+        encoded, encoded_test = preprocess_adult_x_data(x_train, x_test)
+        num_synth_components = 10
+        synth_data = ArrayDataset(
+            kde(NUM_SYNTH_SAMPLES, n_components=num_synth_components, original_data=encoded))
+        original_data_members = ArrayDataset(encoded, y_train)
+        original_data_non_members = ArrayDataset(encoded_test, y_test)
+    elif "nursery" in name:
+        encoded, encoded_test = preprocess_nursery_x_data(x_train, x_test)
+        num_synth_components = 10
+        synth_data = ArrayDataset(
+            kde(NUM_SYNTH_SAMPLES, n_components=num_synth_components, original_data=encoded))
+        original_data_members = ArrayDataset(encoded, y_train)
+        original_data_non_members = ArrayDataset(encoded_test, y_test)
+    else:
+        raise ValueError('Pandas dataset missing a preprocessing step')
+
+    score_g, score_h = mgr.assess(original_data_members, original_data_non_members, synth_data,
+                                  'kde' + str(NUM_SYNTH_SAMPLES) + name)
+
+    assert (score_g.roc_auc_score > 0.5)
+    assert (score_g.average_precision_score > 0.5)
+
+    assert (score_h.share > 0.5)
+
+
+def kde(n_samples, n_components, original_data):
+    """
+    Simple synthetic data genrator: estimates the kernel density of data using a Gaussian kernel and then generates
+    samples from this distribution
+    """
+    digit_data = original_data
+    pca = PCA(n_components=n_components, whiten=False)
+    data = pca.fit_transform(digit_data)
+    params = {'bandwidth': np.logspace(-1, 1, 20)}
+    grid = GridSearchCV(KernelDensity(), params, cv=5)
+    grid.fit(data)
+
+    kde_estimator = grid.best_estimator_
+
+    new_data = kde_estimator.sample(n_samples, random_state=0)
+    new_data = pca.inverse_transform(new_data)
+    return new_data
+
+
+def preprocess_adult_x_data(x_train, x_test):
+    features = ['age', 'workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
+                'capital-gain', 'capital-loss', 'hours-per-week', 'native-country']
+    categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
+                            'native-country']
+    # prepare data for DT
+    numeric_features = [f for f in features if f not in categorical_features]
+    numeric_transformer = Pipeline(
+        steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
+    )
+    categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, numeric_features),
+            ("cat", categorical_transformer, categorical_features),
+        ]
+    )
+    encoded = preprocessor.fit_transform(x_train)
+    encoded_test = preprocessor.fit_transform(x_test)
+    return encoded, encoded_test
+
+
+def preprocess_nursery_x_data(x_train, x_test):
+    x_train = x_train.astype(str)
+    features = ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health"]
+    # QI = ["finance", "social", "health"]
+    categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
+    # prepare data for DT
+    numeric_features = [f for f in features if f not in categorical_features]
+    numeric_transformer = Pipeline(
+        steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
+    )
+    categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, numeric_features),
+            ("cat", categorical_transformer, categorical_features),
+        ]
+    )
+    encoded = preprocessor.fit_transform(x_train)
+    encoded_test = preprocessor.fit_transform(x_test)
+    return encoded, encoded_test