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Counterfeiting in metal part manufacturing has become a major global concern. Although significant effort has been made in detecting the implementation of such counterfeits, modern approaches suffer from high expense during production, invasiveness during manufacture, and unreliability in practice if parts are damaged during use. In this paper, a practical microstructure-based counterfeit detection methodology is proposed, which draws on inherent randomness present in the microstructure as a result of the manufacturing process. An optical Physically Unclonable Function (PUF) protocol is developed which takes a micrograph as input and outputs a compact, unique string representation of the micrograph. The uniqueness of the outputs and their robustness to moderate wear and tear is demonstrated by application of the methodology to brass samples. The protocol is shown to have good discriminatory power even between samples manufactured in the same batch, and runs on the order of several seconds per part on inexpensive machines.

We propose a method for ensuring traceability of metal goods in an efficient and secure manner that leverages data obtained from micrographs of a part’s surface that is instance specific (i.e., different for another instance of that same part). All stakeholders in modern supply chains face a growing need to ensure quality and trust in the goods they produce. Complex supply chains open many opportunities for counterfeiters, saboteurs, or other attackers to infiltrate supply networks, and existing methods for preventing such attacks can be costly, invasive, and ineffective. The proposed method extracts discriminatory-yet-robust intrinsic strings using features extracted from the two-point autocorrelation data of surface microstructures, as well as from local volume fraction data. By using a synthetic dataset of three-phase micrographs similar to those obtained from metal alloy systems using low-cost optical microscopy techniques, we discuss tailoring the method with respect to cost and security and discuss the performance of the method in the context of anticounterfeiting and how similar methods may be evaluated for performance. Cryptographic extensions of this methodology are also discussed.

In this work, we present a simple, yet effective and practical scheme to improve the security of stored password hashes, increasing the difficulty to crack passwords and exposing cracking attempts. We utilize a hardware-dependent function (HDF), such as a physically unclonable function (PUF) or a hardware security module (HSM), at the authentication server to inhibit offline password discovery. Additionally, a deception mechanism is incorporated to alert administrators of cracking attempts. Using an HDF to generate password hashes hinders attackers from recovering the true passwords without constant access to the HDF. Our scheme can integrate with legacy systems without needing additional servers, changing the structure of the hashed password file, nor modifying client machines. When using our scheme, the structure of the hashed passwords file, e.g., etc/shadow or etc/master.passwd , will appear no different than traditional hashed password files. 1 However, when attackers exfiltrate the hashed password file and attempt to crack it, the passwords they will receive are ErsatzPasswords—“fake passwords.” The ErsatzPasswords scheme is flexible by design, enabling it to be integrated into existing authentication systems without changes to user experience. The proposed scheme is integrated into the pam_unix module as well as two client/server authentication schemes: Lightweight Directory Access Protocol (LDAP) authentication and the Pythia pseudorandom function (PRF) Service [Everspaugh et al. 2015]. The core library to support ErsatzPasswords written in C and Python consists of 255 and 103 lines of code, respectively. The integration of ErsatzPasswords into each explored authentication system required less than 100 lines of additional code. Experimental evaluation of ErsatzPasswords shows an increase in authentication latency on the order of 100ms, which maybe acceptable for real world systems. We also describe a framework for implementing ErsatzPasswords using a Trusted Platform Module (TPM).

Designers need a way to overcome information related risks, including information leakage and misuse from their own collaborators during a collaborative product realization process. Existing cryptographic techniques aimed at overcoming these information related risks are computationally expensive and slow even for moderate problem sizes, and legal approaches (e.g., the use of non-disclosure agreements) are not effective. The computational practicality problem is particularly pronounced for simulation computations like finite element analysis (FEA), that involve both a geometric partitioning (meshing) and computations of cubic time complexity. In this paper, we propose a technological approach that enables designers to perform simulations, such as FEA computations, without the need for revealing their information to anyone, including their design collaborators. We demonstrate our approach using secure finite element analysis (sFEA) which enables designers to perform FEA without having to reveal structural/material information to their counterparts even though the computed answer depends on all the collaborators’ confidential information. We build sFEA using computationally efficient protocols implementing a secure co-design framework. One of our findings is that the most natural implementations of sFEA, using existing protocols, suffer from limited scalability. To overcome these limitations, we propose strategies that help improve the scalability of sFEA. We document and discuss the experiments we conducted to determine the computational overhead imposed by sFEA. The results indicate that the computational burden imposed by sFEA makes it challenging for large-scale FEA — our scheme significantly increases the problem sizes that can be handled when compared to implementations using previous algorithms and protocols, but large enough problem sizes will swamp our scheme as well (in some sense this is unavoidable because of the cubic nature of the FEA time complexity). This work is another step towards opening up new avenues for improving the way information is exchanged in collaborative simulation computations such as FEA.

In the context of globalized supply chains, counterfeiting of manufactured goods is a growing problem. The financial, legal, and reputational costs that counterfeit goods impose on legitimate enterprises have spurred investigation into efficient and robust anti-counterfeiting methodologies. In particular, physically unclonable functions (PUFs) have been applied effectively in several manufacturing areas, especially electronics. However, anti-counterfeiting solutions for generic manufactured goods are often expensive to make and implement, or not robust to minor damage that the goods may sustain during transport and use. In this paper, a framework for developing robust, efficient, and cost-effective optical PUFs for anti-counterfeiting of manufactured metallic goods is proposed, along with an example implementation for 4140-steel parts according to standard ASTM A29. For an input library of 50 steel micrographs, the proposed example PUF is shown to have good robustness to simulated part damage and an estimated classification error rate of less than 1%.

The SQL group-by operator plays an important role in summarizing and aggregating large datasets in a data analytic stack.While the standard group-by operator, which is based on equality, is useful in several applications, allowing similarity aware grouping provides a more realistic view on real-world data that could lead to better insights. The Similarity SQL-based Group-By operator (SGB, for short) extends the semantics of the standard SQL Group-by by grouping data with similar but not necessarily equal values. While existing similarity-based grouping operators efficiently materialize this approximate semantics, they primarily focus on one-dimensional attributes and treat multidimensional attributes independently. However, correlated attributes, such as in spatial data, are processed independently, and hence, groups in the multidimensional space are not detected properly. To address this problem, we introduce two new SGB operators for multidimensional data. The first operator is the clique (or distance-to-all) SGB, where all the tuples in a group are within some distance from each other. The second operator is the distance-to-any SGB, where a tuple belongs to a group if the tuple is within some distance from any other tuple in the group. We implement and test the new SGB operators and their algorithms inside PostgreSQL. The overhead introduced by these operators proves to be minimal and the execution times are comparable to those of the standard Group-by. The experimental study, based on TPC-H and a social check-in data, demonstrates that the proposed algorithms can achieve up to three orders of magnitude enhancement in performance over baseline methods developed to solve the same problem., submit to TKDE

Most research today in electronic communications focuses on developing new technologies and “best practices” to enhance security and privacy. However, these technologies and best-practice codes are all too often not used, and in this failure the users are often their own worst enemies. It is thus important to develop an enhanced understanding of why users indicate concern about security and privacy, but when given the opportunity act contrary to their stated intention. We collect data from 435 participants about 24 electronic activities with a survey and a mixed effects model. We find a significant inverse relationship between their assessed risk and benefit (recognizing that the particular technology matters where some technologies are inherently perceived as more risky than others). This significant inverse relationship represents a departure from decision theories that assume purely cognitive information processing, and the separation of the probabilities and the utilities. We shed light on the role of affect, which commonly competes with cognition, and works as an orienting mechanism in security and privacy behaviors, and discuss business and policy implications of our findings.

Erratum to: M.J. Atallah and N. Hopper (Eds.) Privacy Enhancing Technologies DOI: 10.1007/978-3-642-14527-8

In this work we present a simple, yet effective and practical, scheme to improve the security of stored password hashes, rendering their cracking detectable and insuperable at the same time. We utilize a machine-dependent function, such as a physically unclonable function (PUF) or a hardware security module (HSM) at the authentication server to prevent off-site password discovery, and a deception mechanism to alert us if such an action is attempted. Our scheme can be easily integrated with legacy systems without the need of any additional servers, changing the structure of the hashed password file or any client modifications. When using the scheme the structure of the hashed passwords file, etc/shadow or etc/master.passwd, will appear no different than in the traditional scheme.1 However, when an attacker exfiltrates the hashed passwords file and tries to crack it, the only passwords he will get are the ersatzpasswords --- the "fake passwords". When an attempt to login using these ersatzpasswords is detected an alarm will be triggered in the system. Even with an adversary who knows about the scheme, cracking cannot be launched without physical access to the authentication server. The scheme also includes a secure backup mechanism in the event of a failure of the hardware dependent function. We discuss our implementation and provide some discussion in comparison to the traditional authentication scheme.

We adress in this paper the security issues that arise when outsourcing business processes in the BPaaS (Business Process as a Service). In particular when sharing and reusing process fragments coming from different organizations for faster and easier development of process-based applications (PBA). The goal is twofold, to preserve the process fragment provenance, i.e., the companies's business activities which provide the reused fragments in order to avoid the competition, and to guarantee the end-to-end availability of PBA to fragment's consumers. We formally define the problem, and offer an efficient anonymization-based protocol. Experiments have been conducted to show the effectiveness of the proposed solution. We address the security issues that arise when outsourcing business processes in the cloud as BPaaS (Business Process as a Service).We formally define the concept of sharing and reusing process fragments for faster and easier development of process-based applications.We provide an efficient anonymization-based protocol to preserve the process fragment provenance, and to guarantee the end-to-end availability of process-based applications.We evaluate the performance of the proposed approach on real datasets.