Protecting sensitive datasets from insider and outsider attacks has been a major concern over the years. Relational Database Management System (RDBMS) has been the de facto standard to store, retrieve and manage large datasets efficiently in the last few years. However, as surprising as it seems, not a lot of works can be found in the literature which protect databases from anomalous accesses. In this paper, we present a novel Intrusion Detection System (IDS) for relational databases. Our primary objective is to protect databases from both insider and outsider threats by detecting anomalous access patterns using Hidden Markov Model (HMM). While most of the previous notable works in this area focus on query syntax to detect anomalous access, our approach takes into account the amount of sensitive information a query result contains to detect a potential intrusion. Finally, our empirical evaluation on the publicly available TPC-H dataset shows that our IDS can detect anomalous query access with a high degree of accuracy.

Nowadays, huge amount of documents are increasingly transferred to the remote servers due to the appealing features of cloud computing. On the other hand, privacy and security of the sensitive information in untrusted cloud environment is a big concern. To alleviate such concerns, encryption of sensitive data before its transfer to the cloud has become an important risk mitigation option. Encrypted storage provides protection at the expense of a significant increase in the data management complexity. For effective management, it is critical to provide efficient selective document retrieval capability on the encrypted collection. In fact, considerable amount of searchable symmetric encryption schemes have been designed in the literature to achieve this task. However, with the emergence of big data everywhere, available approaches are insufficient to address some crucial real-world problems such as scalability.

To mitigate security concerns of outsourced databases, quite a few protocols have been proposed that outsource data in encrypted format and allow encrypted query execution on the server side. Among the more practical protocols, the "bucketization" approach facilitates query execution at the cost of reduced efficiency by allowing some false positives in the query results. Precise Query Protocols (PQPs), on the other hand, enable the server to execute queries without incurring any false positives. Even though these protocols do not reveal the underlying data, they reveal query access pattern to an adversary. In this paper, we introduce a general attack on PQPs based on access pattern disclosure in the context of secure range queries. Our empirical analysis on several real world datasets shows that the proposed attack is able to disclose significant amount of sensitive data with high accuracy provided that the attacker has reasonable amount of background knowledge. We further demonstrate that a slight variation of such an attack can also be used on imprecise protocols (e.g., bucketization) to disclose significant amount of sensitive information.

In recent years, database as a service (DAS) model where data management is outsourced to cloud service providers has become more prevalent. Although DAS model offers lower cost and flexibility, it necessitates the transfer of potentially sensitive data to untrusted cloud servers. To ensure the confidentiality, encryption of sensitive data before its transfer to the cloud emerges as an important option. Encrypted storage provides protection but it complicates data processing including crucial selective record retrieval. To achieve selective retrieval over encrypted collection, considerable amount of searchable encryption schemes have been proposed in the literature with distinct privacy guarantees. Among the available approaches, oblivious RAM based ones offer optimal privacy. However, they are computationally intensive and do not scale well to very large databases. On the other hand, almost all efficient schemes leak some information, especially data access pattern to the remote servers. Unfortunately, recent evidence on access pattern leakage indicates that adversary's background knowledge could be used to infer the contents of the encrypted data and may potentially endanger individual privacy.

), however, when databases are maintained by disparate organizations, the disclosure of such information can breach the privacy of the corresponding individuals. Various private record linkage (PRL) methods have been developed to obscure such identifiers, but they vary widely in their ability to balance competing goals of accuracy, efficiency and security. The tokenization and hashing of field values into Bloom filters (BF) enables greater linkage accuracy and efficiency than other PRL methods, but the encodings may be compromised through frequency-based cryptanalysis. Our objective is to adapt a BF encoding technique to mitigate such attacks with minimal sacrifices in accuracy and efficiency. To accomplish these goals, we introduce a statistically-informed method to generate BF encodings that integrate bits from multiple fields, the frequencies of which are provably associated with a minimum number of fields. Our method enables a user-specified tradeoff between security and accuracy. We compare our encoding method with other techniques using a public dataset of voter registration records and demonstrate that the increases in security come with only minor losses to accuracy.

The integration of information dispersed among multiple repositories is a crucial step for accurate data analysis in various domains. In support of this goal, it is critical to devise procedures for identifying similar records across distinct data sources. At the same time, to adhere to privacy regulations and policies, such procedures should protect the confidentiality of the individuals to whom the information corresponds. Various private record linkage (PRL) protocols have been proposed to achieve this goal, involving secure multi-party computation (SMC) and similarity preserving data transformation techniques. SMC methods provide secure and accurate solutions to the PRL problem, but are prohibitively expensive in practice, mainly due to excessive computational requirements. Data transformation techniques offer more practical solutions, but incur the cost of information leakage and false matches. In this paper, we introduce a novel model for practical PRL, which 1) affords controlled and limited information leakage, 2) avoids false matches resulting from data transformation. Initially, we partition the data sources into blocks to eliminate comparisons for records that are unlikely to match. Then, to identify matches, we apply an efficient SMC technique between the candidate record pairs. To enable efficiency and privacy, our model leaks a controlled amount of obfuscated data prior to the secure computations. Applied obfuscation relies on differential privacy which provides strong privacy guarantees against adversaries with arbitrary background knowledge. In addition, we illustrate the practical nature of our approach through an empirical analysis with data derived from public voter records.

OBJECTIVE: Integration of patients' records across resources enhances analytics. To address privacy concerns, emerging strategies such as Bloom filter encodings (BFEs), enable integration while obscuring identifiers. However, recent investigations demonstrate BFEs are, in theory, vulnerable to cryptanalysis when encoded identifiers are randomly selected from a public resource. This study investigates the extent to which cryptanalysis conditions hold for (1) real patient records and (2) a countermeasure that obscures the frequencies of the identifying values in encoded datasets. DESIGN: First, to investigate the strength of cryptanalysis for real patient records, we build BFEs from identifiers in an electronic medical record system and apply cryptanalysis using identifiers in a publicly available voter registry. Second, to investigate the countermeasure under ideal cryptanalysis conditions, we compose BFEs from the identifiers that are randomly selected from a public voter registry. MEASUREMENT: We utilize precision (ie, rate of correct re-identified encodings) and computation efficiency (ie, time to complete cryptanalysis) to assess the performance of cryptanalysis in BFEs before and after application of the countermeasure. RESULTS: Cryptanalysis can achieve high precision when the encoded identifiers are composed of a random sample of a public resource (ie, a voter registry). However, we also find that the attack is less efficient and may not be practical for more realistic scenarios. By contrast, the proposed countermeasure made cryptanalysis impractical in terms of precision and efficiency. CONCLUSIONS: Performance of cryptanalysis against BFEs based on patient data is significantly lower than theoretical estimates. The proposed countermeasure makes BFEs resistant to known practical attacks.

In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset.