Data encryption using counting bloom filters for cloud security

An increasing number of architectural techniques have relied on hardware counting bloom filters (CBFs) to improve upon the energy, delay, and complexity of various processor structures. CBFs improve the energy and speed of membership tests by maintaining an imprecise and compact representation of a large set to be searched. This paper studies the energy, delay, and area characteristics of two implementations for CBFs using full custom layouts in a commercial 0.13-mum fabrication technology. One implementation, S-CBF, uses an SRAM array of counts and a shared up/down counter. Our proposed implementation, L-CBF, utilizes an array of up/down linear feedback shift registers and local zero detectors. Circuit simulations show that for a 1 K-entry CBF with a 15-bit count per entry, L-CBF compared to S-CBF is 3.7times or 1.6times faster and requires 2.3times or 1.4times less energy depending on the operation. Additionally, this paper presents analytical energy and delay models for L-CBF. These models can estimate energy and delay of various CBF organizations during architectural level explorations when a physical level implementation is not available. Our results demonstrate that for a variety of L-CBF organizations, the estimations by analytical models are within 5% and 10% of Spectre simulation results for delay and energy, respectively.

The Counting Bloom Filter (CBF) is a kind of space-efficient data structure that extends a Bloom filter so as to allow approximate multiplicity queries on a dynamic multi-set. This paper evaluates the performance of multiplicity queries of three simple CBF schemes-the Naive Counting Bloom Filter (NCBF), the Space-Code Bloom Filter (SCBF) and the d-left Counting Bloom Filter (dlCBF)—using metrics of space complexity and counting error under both uniform and zipfian multiplicity distributions. We compare their counting error under same space complexity, and their space complexity when similar counting errors are achieved respectively. Our results show that dlCBF is the best while SCBF is the worst in terms of both space-efficiency and accuracy. Furthermore, the performance gap between dlCBF and the others has a trend of being enlarged with the increment of space occupation or counting accuracy.

As a key component in implementing Named Data Networking (NDN), Pending Interest Table (PIT) requires an efficient exact-matching algorithm for a scalable and fast PIT lookup. A Bloom filter (BF) is a memory-efficient data structure for performing exact matching operations. In this paper, three different BF-based PIT architectures are proposed: PIT using functional Bloom filters (FBF-PIT), PIT using counting Bloom filters with return values (rCBF-PIT), and a refined rCBF-PIT with signatures (R-rCBF-PIT). The proposed BF-based PITs incrementally allocate a new BF for storing multiple incoming faces of Interest packets with the same content name. For a Data packet lookup, the proposed PIT architectures simultaneously access every BF structure to find matching faces and delete the faces (i.e., matching Interest packet information). The functional Bloom filter (FBF) used in an FBF-PIT is a key-value data structure that stores values only without keys. However, because the number of non-reusable conflict cells in the FBF increases as the number of stored packets increases in the FBF-PIT, the indeterminable rate increases. To decrease the indeterminable rate, we propose the rCBF-PIT, which uses counting Bloom filters with return values (rCBFs), allowing reusable conflict cells. False positives for Interest packets lead to incorrect deletions that can cause false negatives for incoming Data packets. Because most of the false positives occur in the first BF structure, we finally propose the R-rCBF-PIT, in which the first rCBF is replaced with an rCBF with a signature field. The proposed PITs also provide an aging mechanism using a valid bit and a hit bit for entry expiration. Simulation results show that rCBF-PIT and R-rCBF-PIT both reduce the indeterminable rate by more than 81% compared with FBF-PIT. The results also show that R-rCBF-PIT resolves false negatives caused by incorrect deletions by including the signature fields in the first rCBF.

Searchable encryption is an efficient and secure solution that can perform search operation in ciphertext on cloud servers. It is widely used by a growing number of businesses and individuals. However, most existing searchable encryption schemes typically overestimate the security level of the cloud server, which is an untrusted and centralized third party in fact. The emergence of blockchain technology solves the trustworthiness problem of the third party with features such as decentralization, verifiability and immutability. The combination of searchable encryption and blockchain technology uses the smart contract instead of the cloud server to execute ciphertext search task, which improves security and efficiency of search significantly. We propose a Fuzzy Multi-keyword Search Scheme for Encrypted Cloud Data based on Multi-chain Network (FMSM). FMSM is a distributed searchable encryption scheme since we adopt a multi-chain architecture. It not only supports fuzzy multi-keyword search and dynamic updates for encrypted cloud data, but also supports parallel processing of data. In FMSM, counting bloom filter and MinHash algorithm are used to construct index vectors and query vectors on the corresponding two chains, besides, the search is done on a third chain. All of these calculation tasks are executed in the way of distributed computing, which makes the index construction and search process more efficient and the sorting results more accurate. The security analysis and performance evaluation show that the FMSM has high security, reliability, search efficiency and accuracy.

In an intelligent edge network, data owners encrypt data and outsource it to the edge servers to prevent the leakage of data and user information. It is a research issue to achieve efficient search and data update of the ciphertext stored in the edge servers. For the above problems, we construct a fuzzy multi-keyword search scheme based on a two-level tree index, where the first-level tree index stores keyword tags and the second-level stores encrypted file identifiers and counting bloom filters (CBFs). By constructing a two-level tree index, efficient multi-keyword search is realised, and it also has obvious time advantages when performing the single-keyword search. In addition, the proposed scheme supports index updating by introducing the CBF, and it can realise fuzzy search by calculating the inner product between the CBF in index and search trapdoor. At last, the proposed scheme is proved to be semantically secure under the known ciphertext attack model. Theoretical analysis and simulation test indicate that the proposed scheme spends lower computation overhead than other related schemes, especially in the search phase; as the number of matching files or query keywords grows, it costs less time, at around 2–10 ms.

With the wide spread of Cloud service, users are outsourcing their data to the third party servers. When we delegate our data to the cloud servers, there is such a potential risk that the data stored in the servers is stolen. If the data are stored in the encrypted form, the damage caused by data theft is low because the data will not leak to other parties. However, a new problem arises, that is, users are not able to operate encrypted data, which loses the merit of cloud computing. To solve such a problem, searchable symmetric encryption(SSE) is proposed. SSE is a cryptographic protocol that can execute keyword search over encrypted documents without decrypting them. Secure Indexes is one of realizations of SSE that provides constant time keyword search per document with the help of pseudo-random function and Bloom filters. However, Secure Indexes dose not support addition and deletion of documents. Tekin and Sahin proposed an improved Secure Indexes scheme that supports addition and deletion by employing Bloom filter and counting Bloom filter. The drawback of their scheme is that these operations are implemented in user side, which is not desired in the cloud computing since the aim of cloud computing is to delegate as many operations as possible. This paper proposes new SSE scheme. The proposed scheme also employ counting Bloom filter, but provide more fine-grained operations compared to the scheme by Tekin and Sahin in the sense that the proposed scheme provides not only search documents but add and delete the elements of document. Moreover, these operations are executed in the server side. The performance of the proposed SSE is comparable to the existing scheme which provides constant time for search, add and delete per document. We also implemented this SSE scheme, and verified that the scheme performed well not only theoretically but also in practice.

Bloom filters are widely used in many network applications but the high computation cost limits the system performance. In this paper, we introduce a new variation of Bloom filter named One-Hashing Bloom Filter (OHBF) to solve the problem. OHBF requires only one base hash function plus a few simple operations to implement a Bloom filter. While keeping nearly the same theoretical false positive ratio as an ideal Bloom filter, OHBF significantly reduces the hash computation overhead. We show that the false positive performance of a standard Bloom filter implementation strongly relies on the selection of hash functions, even if these hash functions are considered good. In contrast, OHBF presents consistently better performance with a proven mathematical foundation. OHBF is ideal for high throughput and low latency applications. As OHBF is a fundamental technique in Bloom filter theory, it can be applied to many other Bloom filter variations, such as Counting Bloom Filter and Space-Code Bloom Filter.

Bloom filters are probabilistic data structures that are popular in networking for set representation; however, they show an inherent inaccuracy due to false positives. One of the potential attacks on Bloom filters is to pollute them with elements that cause the filter to have a larger false positive probability than under normal operation; Pollution is simple when an attacker knows the details of the filter implementation. Recent research has shown that also black-box adversaries can pollute a counting Bloom filter (a common variant of the filter that also supports removals) with no knowledge of its implementation. As over time, many variants and improvements of Bloom filters have been proposed, it is of interest to study whether they can also be polluted and if so also the increase in their false positive probability. This paper first proposes and then evaluates pollution attacks for some of the most common variants including the Block Bloom filters (BBFs), the Variable Increment and Fingerprint Counting Bloom filters (VI-CBFs and FP-CBFs). The results show that with or without knowledge of the implementation, these variants of the Bloom filter are significantly more vulnerable to pollution attacks than the traditional Bloom filter. In particular, BBFs are extremely vulnerable, so providing an insight on their impact and use in practical systems when the number of memory accesses per lookup must be reduced.

One of the most common threats to internet security is the Denial of Service attack. There are numerous methods and protocol changes in an effort to detect them. The most common DoS attack detection method relies on the number of the TCP control packet in the network flow. The Bloom filter represents a space efficient data structure that is commonly utilized to detect matching pairs. There are multiple algorithms for the DoS attack detection based on the Bloom filter. The SACK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> algorithm uses the SYN/ACK - ACK matching pair detection with the Bloom filter data structure. The false positive error introduced by the Bloom filter influences on the matching pair detection in the algorithm. The improved SACK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> algorithm significantly reduces the false positive error by replacing the Counting Bloom Filter (CBF) data structure with the Dual Counting Bloom Filter (DCBF) data structure. This improvement significantly improves the matching pair detection. It is expected that the false positive error should influence on the detection of the DoS attack. In this paper, the experimental study is performed to analyse this influence. This study confirms the correlation between the false positive error and the DoS attack detection.

Bloom filters are widely used in networking and computing to accelerate membership checking. In many applications filters store sensitive data, so their privacy is of primary concern. At first glance, it seems that extracting the set of elements inserted from the filter would not be possible, because in Bloom filters elements are mapped to positions using hash functions. However, previous works have shown that for the Bloom filter, it may be possible to identify few of the elements inserted in the filter. In this work, we consider the case of counting Bloom filters (CBFs) and show that in some cases, the entire set of elements used to create the filter can be extracted from the filter. This poses serious privacy and security concerns when an attacker can get access to the filter contents. In this article, an algorithm to extract the elements inserted from the filter is presented and analyzed theoretically; then, the feasibility of the CBF inversion is shown by simulation. A case study is presented in detail to illustrate that in practical applications, these conditions can be met by using additional restrictions that are implicit in the nature of the application itself.

In computing, duplicate data detection refers to identifying duplicate copies of repeating data. Identifying duplicate data items in streamed data and eliminating them before storing, is a complex job. This paper proposes a novel data structure for duplicate detection using a variant of stable Bloom filter named as FingerPrint Stable Bloom Filter (FP-SBF). The proposed approach uses counting Bloom filter with fingerprint bits along with an optimization mechanism for duplicate detection. FP-SBF uses d-left hashing which reduces the computational time and decreases the false positives as well as false negatives. FP-SBF can process unbounded data in single pass, using k hash functions, and successfully differentiate between duplicate and distinct elements in O(k+1) time, independent of the size of incoming data. The performance of FP-SBF has been compared with various Bloom Filters used for stream data duplication detection and it has been theoretically and experimentally proved that the proposed approach efficiently detects the duplicates in streaming data with less memory requirements.

Detecting duplicates in click data streams is an important task to fight against click fraud, which is the act of generating false clicks in internet advertising. Revenue generation advertising models, that charges advertisers for each click, leave space for individuals or rival companies to generate false clicks. The extent of click fraud’s damage to online advertising has grown tremendously over the years. In this paper, we consider the problem of detecting duplicates in click data streams. Our solution uses a modified version of the counting Bloom filter. The temporal stateful Bloom filter (TSBF) extends the standard counting Bloom filter by replacing the bit-vector with an array of counters of states. These counters are dynamic and decay with time. We conducted a comprehensive set of experiments using synthetic and real world data. Results are compared with buffering techniques used in NetMosaics, a click fraud detection and prevention solution. Our results show that TSBF approach achieves 99% accuracy on duplicate detection, while keeping its space requirement a constant.

Dynamic adaptive data structures for monitoring data streams

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes can be challenging for many species, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a very useful but underutilized tool for bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π=0.025 (R2=0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π=0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer-based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers.

The continuous scaling-down of the dynamic random access memory (DRAM) manufacturing process has made it possible to improve DRAM density. However, it makes small DRAM cells susceptible to electromagnetic interference between nearby cells. Unless DRAM cells are adequately isolated from each other, the frequent switching access of some cells may lead to unintended bit flips in adjacent cells. This phenomenon is commonly referred to as RowHammer. It is often considered a security issue because unusually frequent accesses to a small set of rows generated by malicious attacks can cause bit flips. Such bit flips may also be caused by general applications. Although several solutions have been proposed, most approaches either incur excessive area overhead or exhibit limited prevention capabilities against maliciously crafted attack patterns. Therefore, the goals of this study are (1) to mitigate RowHammer, even when the number of aggressor rows increases and attack patterns become complicated, and (2) to implement the method with a low area overhead.We propose a robust hardware-based protection method for RowHammer attacks with a low hardware cost called HammerFilter, which employs a modified version of the counting bloom filter. It tracks all attacking rows efficiently by leveraging the fact that the counting bloom filter is a space-efficient data structure, and we add an operation, HALF-DELETE, to mitigate the energy overhead. According to our experimental results, the proposed method can completely prevent bit flips when facing artificially crafted attack patterns (five patterns in our experiments), whereas state-of-the-art probabilistic solutions can only mitigate less than 56% of bit flips on average. Furthermore, the proposed method has a much lower area cost compared to existing counter-based solutions (40.6× better than TWiCe and 2.3× better than Graphene).