DESIGN AND DEVELOPMENT OF KEY REPRESENTATION AUDITING SCHEME FOR SECURE ONLINE AND DYNAMIC STATISTICAL DATABASES

A statistical database (SDB) publishes statistical queries (such as sum, average, count, etc.) on subsets of records. Sometimes by stitching the answers of some statistics, a malicious user (snooper) may be able to deduce confidential information about some individuals. When a user submits a query to statistical database, the difficult problem is how to decide whether the query is answerable or not; to make a decision, past queries must be taken into account, which is called SDB auditing. One of the major drawbacks of the auditing, however, is its excessive CPU time and storage requirements to find and retrieve the relevant records from the SDB. The key representation auditing scheme (KRAS) is proposed to guarantee the security of online and dynamic SDBs. The core idea is to convert the original database into a key representation database (KRDB), also this scheme involves converting each new user query from a string representation into a key representation query (KRQ) and storing it in the Audit Query table (AQ table). Three audit stages are proposed to repel the attacks of the snooper to the confidentiality of the individuals. Also, efficient algorithms for these stages are presented, namely the First Stage Algorithm (FSA), the Second Stage Algorithm (SSA) and the Third Stage Algorithm (TSA). These algorithms enable the key representation auditor (KRA) to conveniently specify the illegal queries which could lead to disclosing the SDB. A comparative study is made between the new scheme and the existing methods, namely a cost estimation and a statistical analysis are performed, and it illustrates the savings in block accesses (CPU time) and storage space that are attainable when a KRDB is used. Finally, an implementation of the new scheme is performed and all the components of the proposed system are discussed

A bayesian approach for on-line max and min auditing

In this paper we consider the on-line max and min query auditing problem: given a private association between fields in a data set, a sequence of max and min queries that have already been posed about the data, their corresponding answers and a new query, deny the answer if a private information is inferred or give the true answer otherwise. We give a probabilistic definition of privacy and demonstrate that max and min queries, without “no duplicates”assumption, can be audited by means of a Bayesian network. Moreover, we show how our auditing approach is able to manage user prior-knowledge

PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn

Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. We focus on the problem of computing robust, reliable analytics in a privacy-preserving manner, while satisfying product requirements. We present PriPeARL, a framework for privacy-preserving analytics and reporting, inspired by differential privacy. We describe the overall design and architecture, and the key modeling components, focusing on the unique challenges associated with privacy, coverage, utility, and consistency. We perform an experimental study in the context of ads analytics and reporting at LinkedIn, thereby demonstrating the tradeoffs between privacy and utility needs, and the applicability of privacy-preserving mechanisms to real-world data. We also highlight the lessons learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information and Knowledge Management (CIKM 2018

Statistical and fuzzy approach for database security

A new type of database anomaly is described by addressing the concept of Cumulated Anomaly in this paper. Dubiety-Determining Model (DDM), which is a detection model basing on statistical and fuzzy set theories for Cumulated Anomaly, is proposed. DDM can measure the dubiety degree of each database transaction quantitatively. Software system architecture to support the DDM for monitoring database transactions is designed. We also implemented the system and tested it. Our experimental results show that the DDM method is feasible and effective

A Model to Overcome Integrity Challenges of an Untrusted DSMS Server

Despite the fact that using the services of outsourced data stream servers has been welcomed extremely, still the problem of obtaining assurance about the received results from these untrusted servers in unsecure environment is one of the basic challenges. In this paper, we present a probabilistic model for auditing received results from an outsourced data stream server through unsecure communication channels. In our architecture, the server is considered as a black box and the auditing process is fulfilled by cooperation between the data stream owner and users. Our method imposes an ignorable overhead on the user and needs no change in the structure of the server. The probabilistic modeling of the system proves algorithms convergence and the experimental evaluations show very acceptable results

Privacy-preserving techniques for computer and network forensics

Clients, administrators, and law enforcement personnel have many privacy concerns when it comes to network forensics. Clients would like to use network services in a freedom-friendly environment that protects their privacy and personal data. Administrators would like to monitor their network, and audit its behavior and functionality for debugging and statistical purposes (which could involve invading the privacy of its network users). Finally, members of law enforcement would like to track and identify any type of digital crimes that occur on the network, and charge the suspects with the appropriate crimes. Members of law enforcement could use some security back doors made available by network administrators, or other forensic tools, that could potentially invade the privacy of network users. In my dissertation, I will be identifying and implementing techniques that each of these entities could use to achieve their goals while preserving the privacy of users on the network. I will show a privacy-preserving implementation of network flow recording that can allow administrators to monitor and audit their network behavior and functionality for debugging and statistical purposes without having this data contain any private information about its users. This implementation is based on identity-based encryption and differential privacy. I will also be showing how law enforcement could use timing channel techniques to fingerprint anonymous servers that are running websites with illegal content and services. Finally I will show the results from a thought experiment about how network administrators can identify pattern-like software that is running on clients\u27 machines remotely without any administrative privileges. The goal of my work is to understand what privileges administrators or law enforcement need to achieve their goals, and the privacy issues inherent in this, and to develop technologies that help administrators and law enforcement achieve their goals while preserving the privacy of network users

Privacy-safe network trace sharing via secure queries

Privacy concerns relating to sharing network traces have traditionally been handled via sanitization, which includes removal of sensitive data and IP address anonymization. We argue that sanitization is a poor solution for data sharing that offers insufficient research utility to users and poor privacy guarantees to data providers. We claim that a better balance in the utility/privacy tradeoff, inherent to network data sharing, can be achieved via a new paradigm we propose: secure queries. In this paradigm, a data owner publishes a query language and an online portal, allowing researchers to submit sets of queries to be run on data. Only certain operations are allowed on certain data fields, and in specific contexts. Query restriction is achieved via the provider’s privacy policy, and enforced by the language’s interpreter. Query results, returned to researchers, consist of aggregate information such as counts, histograms, distributions, etc. and not of individual packets. We discuss why secure queries provide higher privacy guarantees and higher research utility than sanitization, and present a design of the secure query language and a privacy policy