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Das, Rakesh, Chattopadhyay, Anupam, Rahaman, Hafizur.  2019.  Optimizing Quantum Circuits for Modular Exponentiation. 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID). :407–412.

Today's rapid progress in the physical implementation of quantum computers demands scalable synthesis methods to map practical logic designs to quantum architectures. There exist many quantum algorithms which use classical functions with superposition of states. Motivated by recent trends, in this paper, we show the design of quantum circuit to perform modular exponentiation functions using two different approaches. In the design phase, first we generate quantum circuit from a verilog implementation of exponentiation functions using synthesis tools and then apply two different Quantum Error Correction techniques. Finally the circuit is further optimized using the Linear Nearest Neighbor (LNN) Property. We demonstrate the effectiveness of our approach by generating a set of networks for the reversible modular exponentiation function for a set of input values. At the end of the work, we have summarized the obtained results, where a cost analysis over our developed approaches has been made. Experimental results show that depending on the choice of different QECC methods the performance figures can vary by up to 11%, 10%, 8% in T-count, number of qubits, number of gates respectively.

Das, S., Wei Zhang, Yang Liu.  2014.  Reconfigurable Dynamic Trusted Platform Module for Control Flow Checking. VLSI (ISVLSI), 2014 IEEE Computer Society Annual Symposium on. :166-171.

Trusted Platform Module (TPM) has gained its popularity in computing systems as a hardware security approach. TPM provides the boot time security by verifying the platform integrity including hardware and software. However, once the software is loaded, TPM can no longer protect the software execution. In this work, we propose a dynamic TPM design, which performs control flow checking to protect the program from runtime attacks. The control flow checker is integrated at the commit stage of the processor pipeline. The control flow of program is verified to defend the attacks such as stack smashing using buffer overflow and code reuse. We implement the proposed dynamic TPM design in FPGA to achieve high performance, low cost and flexibility for easy functionality upgrade based on FPGA. In our design, neither the source code nor the Instruction Set Architecture (ISA) needs to be changed. The benchmark simulations demonstrate less than 1% of performance penalty on the processor, and an effective software protection from the attacks.

Das, Saikat, Mahfouz, Ahmed M., Venugopal, Deepak, Shiva, Sajjan.  2019.  DDoS Intrusion Detection Through Machine Learning Ensemble. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :471–477.
Distributed Denial of Service (DDoS) attacks have been the prominent attacks over the last decade. A Network Intrusion Detection System (NIDS) should seamlessly configure to fight against these attackers' new approaches and patterns of DDoS attack. In this paper, we propose a NIDS which can detect existing as well as new types of DDoS attacks. The key feature of our NIDS is that it combines different classifiers using ensemble models, with the idea that each classifier can target specific aspects/types of intrusions, and in doing so provides a more robust defense mechanism against new intrusions. Further, we perform a detailed analysis of DDoS attacks, and based on this domain-knowledge verify the reduced feature set [27, 28] to significantly improve accuracy. We experiment with and analyze NSL-KDD dataset with reduced feature set and our proposed NIDS can detect 99.1% of DDoS attacks successfully. We compare our results with other existing approaches. Our NIDS approach has the learning capability to keep up with new and emerging DDoS attack patterns.
Das, Sauvik, Laput, Gierad, Harrison, Chris, Hong, Jason I..  2017.  Thumprint: Socially-Inclusive Local Group Authentication Through Shared Secret Knocks. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. :3764–3774.

Small, local groups who share protected resources (e.g., families, work teams, student organizations) have unmet authentication needs. For these groups, existing authentication strategies either create unnecessary social divisions (e.g., biometrics), do not identify individuals (e.g., shared passwords), do not equitably distribute security responsibility (e.g., individual passwords), or make it difficult to share or revoke access (e.g., physical keys). To explore an alternative, we designed Thumprint: inclusive group authentication with a shared secret knock. All group members share one secret knock, but individual expressions of the secret are discernible. We evaluated the usability and security of our concept through two user studies with 30 participants. Our results suggest that (1) individuals who enter the same shared thumprint are distinguishable from one another, (2) that people can enter thumprints consistently over time, and (3) that thumprints are resilient to casual adversaries.

Das, Sima, Panda, Ganapati.  2020.  An Initiative Towards Privacy Risk Mitigation Over IoT Enabled Smart Grid Architecture. 2020 International Conference on Renewable Energy Integration into Smart Grids: A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG). :168—173.
The Internet of Things (IoT) has transformed many application domains with realtime, continuous, automated control and information transmission. The smart grid is one such futuristic application domain in execution, with a large-scale IoT network as its backbone. By leveraging the functionalities and characteristics of IoT, the smart grid infrastructure benefits not only consumers, but also service providers and power generation organizations. The confluence of IoT and smart grid comes with its own set of challenges. The underlying cyberspace of IoT, though facilitates communication (information propagation) among devices of smart grid infrastructure, it undermines the privacy at the same time. In this paper we propose a new measure for quantifying the probability of privacy leakage based on the behaviors of the devices involved in the communication process. We construct a privacy stochastic game model based on the information shared by the device, and the access to the compromised device. The existence of Nash Equilibrium strategy of the game is proved theoretically. We experimentally validate the effectiveness of the privacy stochastic game model.
Das, Subhajit, Mondal, Satyendra Nath, Sanyal, Manas.  2019.  A Novel Approach of Image Encryption Using Chaos and Dynamic DNA Sequence. 2019 Amity International Conference on Artificial Intelligence (AICAI). :876–880.
In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.
Das, Subhasis, Aamodt, Tor M., Dally, William J..  2015.  Reuse Distance-Based Probabilistic Cache Replacement. ACM Trans. Archit. Code Optim.. 12:33:1–33:22.

This article proposes Probabilistic Replacement Policy (PRP), a novel replacement policy that evicts the line with minimum estimated hit probability under optimal replacement instead of the line with maximum expected reuse distance. The latter is optimal under the independent reference model of programs, which does not hold for last-level caches (LLC). PRP requires 7% and 2% metadata overheads in the cache and DRAM respectively. Using a sampling scheme makes DRAM overhead negligible, with minimal performance impact. Including detailed overhead modeling and equal cache areas, PRP outperforms SHiP, a state-of-the-art LLC replacement algorithm, by 4% for memory-intensive SPEC-CPU2006 benchmarks.

Das, T., Eldosouky, A. R., Sengupta, S..  2020.  Think Smart, Play Dumb: Analyzing Deception in Hardware Trojan Detection Using Game Theory. 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.
In recent years, integrated circuits (ICs) have become significant for various industries and their security has been given greater priority, specifically in the supply chain. Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multi-level game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zero-sum, repeated game using prospect theory (PT) that captures different players' rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender learns about the attacker's tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by "playing dumb" in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker's view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected.
DaSilva, Gianni, Loud, Vincent, Salazar, Ana, Soto, Jeff, Elleithy, Abdelrahman.  2019.  Context-Oriented Privacy Protection in Wireless Sensor Networks. 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). :1–4.
As more devices become connected to the internet and new technologies emerge to connect them, security must keep up to protect data during transmission and at rest. Several instances of security breaches have forced many companies to investigate the effectiveness of their security measures. In this paper, we discuss different methodologies for protecting data as it relates to wireless sensor networks (WSNs). Data collected from these sensors range in type from location data of an individual to surveillance for military applications. We propose a solution that protects the location of the base station and the nodes while transmitting data.
Datta, A., Kar, S., Sinopoli, B., Weerakkody, S..  2016.  Accountability in cyber-physical systems. 2016 Science of Security for Cyber-Physical Systems Workshop (SOSCYPS). :1–3.

Our position is that a key component of securing cyber-physical systems (CPS) is to develop a theory of accountability that encompasses both control and computing systems. We envision that a unified theory of accountability in CPS can be built on a foundation of causal information flow analysis. This theory will support design and analysis of mechanisms at various stages of the accountability regime: attack detection, responsibility-assignment (e.g., attack identification or localization), and corrective measures (e.g., via resilient control) As an initial step in this direction, we summarize our results on attack detection in control systems. We use the Kullback-Liebler (KL) divergence as a causal information flow measure. We then recover, using information flow analyses, a set of existing results in the literature that were previously proved using different techniques. These results cover passive detection, stealthy attack characterization, and active detection. This research direction is related to recent work on accountability in computational systems [1], [2], [3], [4]. We envision that by casting accountability theories in computing and control systems in terms of causal information flow, we can provide a common foundation to develop a theory for CPS that compose elements from both domains.

Datta, Amarjit, Rahman, Mohammad Ashiqur.  2017.  Cyber Threat Analysis Framework for the Wind Energy Based Power System. Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy. :81–92.
Wind energy is one of the major sources of renewable energy. Countries around the world are increasingly deploying large wind farms that can generate a significant amount of clean energy. A wind farm consists of many turbines, often spread across a large geographical area. Modern wind turbines are equipped with meteorological sensors. The wind farm control center monitors the turbine sensors and adjusts the power generation parameters for optimal power production. The turbine sensors are prone to cyberattacks and with the evolving of large wind farms and their share in the power generation, it is crucial to analyze such potential cyber threats. In this paper, we present a formal framework to verify the impact of false data injection attack on the wind farm meteorological sensor measurements. The framework designs this verification as a maximization problem where the adversary's goal is to maximize the wind farm power production loss with its limited attack capability. Moreover, the adversary wants to remain stealthy to the wind farm bad data detection mechanism while it is launching its cyberattack on the turbine sensors. We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on synthetic test cases.
Datta, Anupam, Fredrikson, Matthew, Ko, Gihyuk, Mardziel, Piotr, Sen, Shayak.  2017.  Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1193–1210.

This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.

Datta, E., Goyal, N..  2014.  Security attack mitigation framework for the cloud. Reliability and Maintainability Symposium (RAMS), 2014 Annual. :1-6.

Cloud computing brings in a lot of advantages for enterprise IT infrastructure; virtualization technology, which is the backbone of cloud, provides easy consolidation of resources, reduction of cost, space and management efforts. However, security of critical and private data is a major concern which still keeps back a lot of customers from switching over from their traditional in-house IT infrastructure to a cloud service. Existence of techniques to physically locate a virtual machine in the cloud, proliferation of software vulnerability exploits and cross-channel attacks in-between virtual machines, all of these together increases the risk of business data leaks and privacy losses. This work proposes a framework to mitigate such risks and engineer customer trust towards enterprise cloud computing. Everyday new vulnerabilities are being discovered even in well-engineered software products and the hacking techniques are getting sophisticated over time. In this scenario, absolute guarantee of security in enterprise wide information processing system seems a remote possibility; software systems in the cloud are vulnerable to security attacks. Practical solution for the security problems lies in well-engineered attack mitigation plan. At the positive side, cloud computing has a collective infrastructure which can be effectively used to mitigate the attacks if an appropriate defense framework is in place. We propose such an attack mitigation framework for the cloud. Software vulnerabilities in the cloud have different severities and different impacts on the security parameters (confidentiality, integrity, and availability). By using Markov model, we continuously monitor and quantify the risk of compromise in different security parameters (e.g.: change in the potential to compromise the data confidentiality). Whenever, there is a significant change in risk, our framework would facilitate the tenants to calculate the Mean Time to Security Failure (MTTSF) cloud and allow them to adopt a dynamic mitigation plan. This framework is an add-on security layer in the cloud resource manager and it could improve the customer trust on enterprise cloud solutions.

Dattana, Vishal, Gupta, Kishu, Kush, Ashwani.  2019.  A Probability based Model for Big Data Security in Smart City. 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC). :1—6.

Smart technologies at hand have facilitated generation and collection of huge volumes of data, on daily basis. It involves highly sensitive and diverse data like personal, organisational, environment, energy, transport and economic data. Data Analytics provide solution for various issues being faced by smart cities like crisis response, disaster resilience, emergence management, smart traffic management system etc.; it requires distribution of sensitive data among various entities within or outside the smart city,. Sharing of sensitive data creates a need for efficient usage of smart city data to provide smart applications and utility to the end users in a trustworthy and safe mode. This shared sensitive data if get leaked as a consequence can cause damage and severe risk to the city's resources. Fortification of critical data from unofficial disclosure is biggest issue for success of any project. Data Leakage Detection provides a set of tools and technology that can efficiently resolves the concerns related to smart city critical data. The paper, showcase an approach to detect the leakage which is caused intentionally or unintentionally. The model represents allotment of data objects between diverse agents using Bigraph. The objective is to make critical data secure by revealing the guilty agent who caused the data leakage.

Dauber, Edwin, Caliskan, Aylin, Harang, Richard, Greenstadt, Rachel.  2018.  Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings. :356-357.

Program authorship attribution has implications for the privacy of programmers who wish to contribute code anonymously. While previous work has shown that complete files that are individually authored can be attributed, these efforts have focused on ideal data sets such as the Google Code Jam data. We explore the problem of attribution "in the wild," examining source code obtained from open source version control systems, and investigate if and how such contributions can be attributed to their authors, either individually or on a per-account basis. In this work we show that accounts belonging to open source contributors containing short, incomplete, and typically uncompilable fragments can be effectively attributed.

Dauda, Ahmed, Mclean, Scott, Almehmadi, Abdulaziz, El-Khatib, Khalil.  2018.  Big Data Analytics Architecture for Security Intelligence. Proceedings of the 11th International Conference on Security of Information and Networks. :19:1-19:4.

The need for security1 continues to grow in distributed computing. Today's security solutions require greater scalability and convenience in cloud-computing architectures, in addition to the ability to store and process larger volumes of data to address very sophisticated attacks. This paper explores some of the existing architectures for big data intelligence analytics, and proposes an architecture that promises to provide greater security for data intensive environments. The architecture is designed to leverage the wealth in the multi-source information for security intelligence.

Dauenhauer, Ralf, Müller, Tobias.  2016.  An Evaluation of Information Connection in Augmented Reality for 3D Scenes with Occlusion. 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct). :235—237.
Most augmented reality applications connect virtual information to anchors, i.e. physical places or objects, by using spatial overlays or proximity. However, for industrial use cases this is not always feasible because specific parts must remain fully visible in order to meet work or security requirements. In these situations virtual information must be displayed at alternative positions while connections to anchors must still be clearly recognizable. In our previous research we were the first to show that for simple scenes connection lines are most suitable for this. To extend these results to more complex environments, we conducted an experiment on the effects of visual interruptions in connection lines and incorrect occlusion. Completion time and subjective mental effort for search tasks were used as measures. Our findings confirm that also in 3D scenes with partial occlusion connection lines are preferable to connect virtual information with anchors if an assignment via overlay or close proximity is not feasible. The results further imply that neither incorrectly used depth cues nor missing parts of connection lines make a significant difference concerning completion time or subjective mental effort. For designers of industrial augmented reality applications this means that they can choose either visualization based on their needs.
Dauterman, Emma, Corrigan-Gibbs, Henry, Mazières, David, Boneh, Dan, Rizzo, Dominic.  2019.  True2F: Backdoor-Resistant Authentication Tokens. 2019 IEEE Symposium on Security and Privacy (SP). :398–416.
We present True2F, a system for second-factor authentication that provides the benefits of conventional authentication tokens in the face of phishing and software compromise, while also providing strong protection against token faults and backdoors. To do so, we develop new lightweight two-party protocols for generating cryptographic keys and ECDSA signatures, and we implement new privacy defenses to prevent cross-origin token-fingerprinting attacks. To facilitate real-world deployment, our system is backwards-compatible with today's U2F-enabled web services and runs on commodity hardware tokens after a firmware modification. A True2F-protected authentication takes just 57ms to complete on the token, compared with 23ms for unprotected U2F.
Davari, Maryam, Bertino, Elisa.  2018.  Reactive Access Control Systems. Proceedings of the 23Nd ACM on Symposium on Access Control Models and Technologies. :205-207.

In context-aware applications, user's access privileges rely on both user's identity and context. Access control rules are usually statically defined while contexts and the system state can change dynamically. Changes in contexts can result in service disruptions. To address this issue, this poster proposes a reactive access control system that associates contingency plans with access control rules. Risk scores are also associated with actions part of the contingency plans. Such risks are estimated by using fuzzy inference. Our approach is cast into the XACML reference architecture.

Dave, Avani, Banerjee, Nilanjan, Patel, Chintan.  2020.  SRACARE: Secure Remote Attestation with Code Authentication and Resilience Engine. 2020 IEEE International Conference on Embedded Software and Systems (ICESS). :1—8.

Recent technological advancements have enabled proliferated use of small embedded and IoT devices for collecting, processing, and transferring the security-critical information and user data. This exponential use has acted as a catalyst in the recent growth of sophisticated attacks such as the replay, man-in-the-middle, and malicious code modification to slink, leak, tweak or exploit the security-critical information in malevolent activities. Therefore, secure communication and software state assurance (at run-time and boot-time) of the device has emerged as open security problems. Furthermore, these devices need to have an appropriate recovery mechanism to bring them back to the known-good operational state. Previous researchers have demonstrated independent methods for attack detection and safeguard. However, the majority of them lack in providing onboard system recovery and secure communication techniques. To bridge this gap, this manuscript proposes SRACARE - a framework that utilizes the custom lightweight, secure communication protocol that performs remote/local attestation, and secure boot with an onboard resilience recovery mechanism to protect the devices from the above-mentioned attacks. The prototype employs an efficient lightweight, low-power 32-bit RISC-V processor, secure communication protocol, code authentication, and resilience engine running on the Artix 7 Field Programmable Gate Array (FPGA) board. This work presents the performance evaluation and state-of-the-art comparison results, which shows promising resilience to attacks and demonstrate the novel protection mechanism with onboard recovery. The framework achieves these with only 8% performance overhead and a very small increase in hardware-software footprint.

Dave, Jay, Das, Manik Lal.  2016.  Securing SQL with Access Control for Database As a Service Model. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. :104:1–104:6.

'Software as a service - SaaS' is a well known model used in cloud infrastructure, outsourcing and pervasive computing. With the SaaS model, application service providers (ASP) facilitates various functionalities of software to application developers as well as to consumers over a public channel like Internet. In order to manage large volumes of users data, 'Database as a service - DaaS' model is a practical requirement for ASPs. The DaaS model allows implementation of need-based (e.g., role-based) privileges of database access to its users. However, the use of DaaS model raises security concerns (e.g. confidentiality and integrity of data) of data while storing users data in untrusted public storage server. In this paper, we review one DaaS tool, CryptDB [1], developed in recent times, and we observe some limitations in it and then present an improved solution for securing data in untrusted database provider. The proposed solution mitigates the limitations of CryptDB while keeping the efficiency of the service model used between ASP and DB intact.

Davenport, Amanda, Shetty, Sachin.  2019.  Air Gapped Wallet Schemes and Private Key Leakage in Permissioned Blockchain Platforms. 2019 IEEE International Conference on Blockchain (Blockchain). :541—545.

In this paper we consider the threat surface and security of air gapped wallet schemes for permissioned blockchains as preparation for a Markov based mathematical model, and quantify the risk associated with private key leakage. We identify existing threats to the wallet scheme and existing work done to both attack and secure the scheme. We provide an overview the proposed model and outline justification for our methods. We follow with next steps in our remaining work and the overarching goals and motivation for our methods.

Davenport, Amanda, Shetty, Sachin.  2019.  Modeling Threat of Leaking Private Keys from Air-Gapped Blockchain Wallets. 2019 IEEE International Smart Cities Conference (ISC2). :9—13.

In this paper we consider the threat surface and security of air gapped wallet schemes for permissioned blockchains as preparation for a Markov based mathematical model, and quantify the risk associated with private key leakage. We identify existing threats to the wallet scheme and existing work done to both attack and secure the scheme. We provide an overview the proposed model and outline justification for our methods. We follow with next steps in our remaining work and the overarching goals and motivation for our methods.

David Nicol, University of Illinois at Urbana-Champaign, Vikas Mallapura, University of Illinois at Urbana-Champaign.  2014.  Modeling and Analysis of Stepping Stone Attacks. 2014 Winter Simulation Conference.

Computer exploits often involve an attacker being able to compromise a sequence of hosts, creating a chain of "stepping stones" from his source to ultimate target. Stepping stones are usually necessary to access well-protected resources, and also serve to mask the attacker’s location. This paper describes means of constructing models of networks and the access control mechanisms they employ to approach the problem of finding which stepping stone paths are easiest for an attacker to find. While the simplest formulation of the problem can be addressed with deterministic shortest-path algorithms, we argue that consideration of what and how an attacker may (or may not) launch from a compromised host pushes one towards solutions based on Monte Carlo sampling. We describe the sampling algorithm and some preliminary results obtained using it.

Davidson, Alex, Fenn, Gregory, Cid, Carlos.  2016.  A Model for Secure and Mutually Beneficial Software Vulnerability Sharing. Proceedings of the 2016 ACM on Workshop on Information Sharing and Collaborative Security. :3–14.

In this work we propose a model for conducting efficient and mutually beneficial information sharing between two competing entities, focusing specifically on software vulnerability sharing. We extend the two-stage game-theoretic model proposed by Khouzani et al. [18] for bug sharing, addressing two key features: we allow security information to be associated with different categories and severities, but also remove a large proportion of player homogeneity assumptions the previous work makes. We then analyse how these added degrees of realism affect the trading dynamics of the game. Secondly, we develop a new private set operation (PSO) protocol that enables the removal of the trusted mediation requirement. The PSO functionality allows for bilateral trading between the two entities up to a mutually agreed threshold on the value of information shared, keeping all other input information secret. The protocol scales linearly with set sizes and we give an implementation that establishes the practicality of the design for varying input parameters. The resulting model and protocol provide a framework for practical and secure information sharing between competing entities.