diff --git a/DIRECTORY.md b/DIRECTORY.md
index d4db021e6..19ceab1e4 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -41,6 +41,7 @@
   * [Binary Search Tree](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/data_structures/binary_search_tree.cpp)
   * [Binary Search Tree2](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/data_structures/binary_search_tree2.cpp)
   * [Binaryheap](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/data_structures/binaryheap.cpp)
+  * [Bloom Filter](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/data_structures/bloom_filter.cpp)
   * [Circular Queue Using Linked List](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/data_structures/circular_queue_using_linked_list.cpp)
   * Cll
     * [Cll](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/data_structures/cll/cll.cpp)
diff --git a/data_structures/bloom_filter.cpp b/data_structures/bloom_filter.cpp
new file mode 100644
index 000000000..8ce1a0c78
--- /dev/null
+++ b/data_structures/bloom_filter.cpp
@@ -0,0 +1,291 @@
+/**
+ * @file
+ * @brief [Bloom Filter](https://en.wikipedia.org/wiki/Bloom_filter)
+ * generic implementation in C++
+ * @details A Bloom filter is a space-efficient probabilistic data structure,
+ * a query returns either "possibly in set" or "definitely not in set".
+ *
+ * More generally, fewer than 10 bits per element are required for a 1% false
+ * positive probability, independent of the size or number of elements in the
+ * set.
+ *
+ * It helps us to not make an "expensive operations", like disk IO - we can
+ * use bloom filter to check incoming request, and with a good probability
+ * get an answer of bloom filter, that we don't need to make our "expensive
+ * operation"
+ *
+ *
+ * [Very good use case example](https://stackoverflow.com/a/30247022)
+ *
+ * Basic bloom filter doesn't support deleting of elements, so
+ * we don't need to implement deletion in bloom filter and bitset in our case.
+ * @author [DanArmor](https://github.com/DanArmor)
+ */
+
+#include <cassert>           /// for assert
+#include <functional>        /// for list of hash functions for bloom filter constructor
+#include <initializer_list>  /// for initializer_list for bloom filter constructor
+#include <string>            /// for testing on strings
+#include <vector>            /// for std::vector
+#include <iostream>          /// for IO operations
+
+/**
+ * @namespace data_structures
+ * @brief Data Structures algorithms
+ */
+namespace data_structures {
+/**
+ * @brief Simple bitset implementation for bloom filter
+ */
+class Bitset {
+ private:
+    std::vector<std::size_t> data;  ///< short info of this variable
+    static const std::size_t blockSize =
+        sizeof(std::size_t);  ///< size of integer type, that we are using in
+                              ///< our bitset
+ public:
+    explicit Bitset(std::size_t);
+    std::size_t size();
+    void add(std::size_t);
+    bool contains(std::size_t);
+};
+
+/**
+ * @brief Utility function to return the size of the inner array
+ * @returns the size of inner array
+ */
+std::size_t Bitset::size() { return data.size(); }
+
+/**
+ * @brief BitSet class constructor
+ * @param initSize amount of blocks, each contain sizeof(std::size_t) bits
+ */
+Bitset::Bitset(std::size_t initSize) : data(initSize) {}
+
+/**
+ * @brief Turn bit on position x to 1s
+ *
+ * @param x position to turn bit on
+ * @returns void
+ */
+void Bitset::add(std::size_t x) {
+    std::size_t blockIndex = x / blockSize;
+    if (blockIndex >= data.size()) {
+        data.resize(blockIndex + 1);
+    }
+    data[blockIndex] |= 1 << (x % blockSize);
+}
+
+/**
+ * @brief Doest bitset contains element x
+ *
+ * @param x position in bitset to check
+ * @returns true if bit position x is 1
+ * @returns false if bit position x is 0
+ */
+bool Bitset::contains(std::size_t x) {
+    std::size_t blockIndex = x / blockSize;
+    if (blockIndex >= data.size()) {
+        return false;
+    }
+    return data[blockIndex] & (1 << (x % blockSize));
+}
+
+/**
+ * @brief Bloom filter template class
+ * @tparam T type of elements that we need to filter
+ */
+template <typename T>
+class BloomFilter {
+ private:
+    Bitset set;  ///< inner bitset for elements
+    std::vector<std::function<std::size_t(T)>>
+        hashFunks;  ///< hash functions for T type
+
+ public:
+    BloomFilter(std::size_t,
+                std::initializer_list<std::function<std::size_t(T)>>);
+    void add(T);
+    bool contains(T);
+};
+
+/**
+ * @brief Constructor for Bloom filter
+ *
+ * @tparam T type of elements that we need to filter
+ * @param size initial size of Bloom filter
+ * @param funks hash functions for T type
+ * @returns none
+ */
+template <typename T>
+BloomFilter<T>::BloomFilter(
+    std::size_t size,
+    std::initializer_list<std::function<std::size_t(T)>> funks)
+    : set(size), hashFunks(funks) {}
+
+/**
+ * @brief Add function for Bloom filter
+ *
+ * @tparam T type of elements that we need to filter
+ * @param x element to add to filter
+ * @returns void
+ */
+template <typename T>
+void BloomFilter<T>::add(T x) {
+    for (std::size_t i = 0; i < hashFunks.size(); i++) {
+        set.add(hashFunks[i](x) % (sizeof(std::size_t) * set.size()));
+    }
+}
+
+/**
+ * @brief Check element function for Bloom filter
+ *
+ * @tparam T type of elements that we need to filter
+ * @param x element to check in filter
+ * @return true if the element probably appears in the filter
+ * @return false if the element certainly does not appear in the filter
+ */
+template <typename T>
+bool BloomFilter<T>::contains(T x) {
+    for (std::size_t i = 0; i < hashFunks.size(); i++) {
+        if (set.contains(hashFunks[i](x) %
+                         (sizeof(std::size_t) * set.size())) == false) {
+            return false;
+        }
+    }
+    return true;
+}
+
+/**
+ * @brief [Function djb2](http://www.cse.yorku.ca/~oz/hash.html)
+ * to get hash for the given string.
+ *
+ * @param s string to get hash from
+ * @returns hash for a string
+ */
+static std::size_t hashDJB2(std::string const& s) {
+    std::size_t hash = 5381;
+    for (char c : s) {
+        hash = ((hash << 5) + hash) + c;
+    }
+    return hash;
+}
+
+/**
+ * @brief [Hash
+ * function](https://stackoverflow.com/questions/8317508/hash-function-for-a-string),
+ * to get hash for the given string.
+ *
+ * @param s string to get hash from
+ * @returns hash for the given string
+ */
+static std::size_t hashStr(std::string const& s) {
+    std::size_t hash = 37;
+    std::size_t primeNum1 = 54059;
+    std::size_t primeNum2 = 76963;
+    for (char c : s) {
+        hash = (hash * primeNum1) ^ (c * primeNum2);
+    }
+    return hash;
+}
+
+/**
+ * @brief [Hash function for
+ * test](https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key)
+ *
+ * @param x to get hash from
+ * @returns hash for the `x` parameter
+ */
+std::size_t hashInt_1(int x) {
+    x = ((x >> 16) ^ x) * 0x45d9f3b;
+    x = ((x >> 16) ^ x) * 0x45d9f3b;
+    x = (x >> 16) ^ x;
+    return x;
+}
+
+/**
+ * @brief [Hash function for
+ * test](https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key)
+ *
+ * @param x to get hash from
+ * @returns hash for the `x` parameter
+ */
+std::size_t hashInt_2(int x) {
+    auto y = static_cast<std::size_t>(x);
+    y = (y ^ (y >> 30)) * static_cast<std::size_t>(0xbf58476d1ce4e5b9);
+    y = (y ^ (y >> 27)) * static_cast<std::size_t>(0x94d049bb133111eb);
+    y = y ^ (y >> 31);
+    return y;
+}
+}  // namespace data_structures
+
+/**
+ * @brief Test for bloom filter with string as generic type
+ * @returns void
+ */
+static void test_bloom_filter_string() {
+    data_structures::BloomFilter<std::string> filter(
+        10, {data_structures::hashDJB2, data_structures::hashStr});
+    std::vector<std::string> toCheck{"hello", "world", "!"};
+    std::vector<std::string> toFalse{"false", "world2", "!!!"};
+    for (auto& x : toCheck) {
+        filter.add(x);
+    }
+    for (auto& x : toFalse) {
+        assert(filter.contains(x) == false);
+    }
+    for (auto& x : toCheck) {
+        assert(filter.contains(x));
+    }
+}
+
+/**
+ * @brief Test for bloom filter with int as generic type
+ * @returns void
+ */
+static void test_bloom_filter_int() {
+    data_structures::BloomFilter<int> filter(
+        20, {data_structures::hashInt_1, data_structures::hashInt_2});
+    std::vector<int> toCheck{100, 200, 300, 50};
+    std::vector<int> toFalse{1, 2, 3, 4, 5, 6, 7, 8};
+    for (int x : toCheck) {
+        filter.add(x);
+    }
+    for (int x : toFalse) {
+        assert(filter.contains(x) == false);
+    }
+    for (int x : toCheck) {
+        assert(filter.contains(x));
+    }
+}
+
+/**
+ * @brief Test for bitset
+ *
+ * @returns void
+ */
+static void test_bitset() {
+    data_structures::Bitset set(2);
+    std::vector<std::size_t> toCheck{0, 1, 5, 8, 63, 64, 67, 127};
+    for (auto x : toCheck) {
+        set.add(x);
+        assert(set.contains(x));
+    }
+    assert(set.contains(128) == false);
+    assert(set.contains(256) == false);
+}
+
+/**
+ * @brief Main function
+ * @returns 0 on exit
+ */
+int main() {
+    // run self-test implementations
+
+    test_bitset();  // run test for bitset, because bloom filter is depending on it
+    test_bloom_filter_string();
+    test_bloom_filter_int();
+    
+    std::cout << "All tests have successfully passed!\n";
+    return 0;
+}