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1. Differentially private binary- and matrix-valued data query: an XOR mechanism

   https://doi.org/10.14778/3446095.3446106  

   Ji, Tianxi ; Li, Pan ; Yilmaz, Emre ; Ayday, Erman ; Ye, Yanfang ; Sun, Jinyuan ( January 2021 , Proceedings of the VLDB Endowment)

   null (Ed.)

   Differential privacy has been widely adopted to release continuous- and scalar-valued information on a database without compromising the privacy of individual data records in it. The problem of querying binary- and matrix-valued information on a database in a differentially private manner has rarely been studied. However, binary- and matrix-valued data are ubiquitous in real-world applications, whose privacy concerns may arise under a variety of circumstances. In this paper, we devise an exclusive or (XOR) mechanism that perturbs binary- and matrix-valued query result by conducting an XOR operation on the query result with calibrated noises attributed to a matrix-valued Bernoulli distribution. We first rigorously analyze the privacy and utility guarantee of the proposed XOR mechanism. Then, to generate the parameters in the matrix-valued Bernoulli distribution, we develop a heuristic approach to minimize the expected square query error rate under ϵ -differential privacy constraint. Additionally, to address the intractability of calculating the probability density function (PDF) of this distribution and efficiently generate samples from it, we adapt an Exact Hamiltonian Monte Carlo based sampling scheme. Finally, we experimentally demonstrate the efficacy of the XOR mechanism by considering binary data classification and social network analysis, all in a differentially private manner. Experiment results show that the XOR mechanism notably outperforms other state-of-the-art differentially private methods in terms of utility (such as classification accuracy and F 1 score), and even achieves comparable utility to the non-private mechanisms. 

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2. Achieving Differential Privacy in Vertically Partitioned Multiparty Learning

   https://doi.org/10.1109/bigdata52589.2021.9671502  

   Xu, Depeng ; Yuan, Shuhan ; Wu, Xintao ( December 2021 , 2021 IEEE International Conference on Big Data (Big Data))

   Preserving differential privacy has been well studied under the centralized setting. However, it’s very challenging to preserve differential privacy under multiparty setting, especially for the vertically partitioned case. In this work, we propose a new framework for differential privacy preserving multiparty learning in the vertically partitioned setting. Our core idea is based on the functional mechanism that achieves differential privacy of the released model by adding noise to the objective function. We show the server can simply dissect the objective function into single-party and cross-party sub-functions, and allocate computation and perturbation of their polynomial coefficients t o l ocal p arties. Our method n eeds o nly o ne r ound of noise addition and secure aggregation. The released model in our framework achieves the same utility as applying the functional mechanism in the centralized setting. Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method. 

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3. Privacy-Preserving Policy Synthesis in Markov Decision Processes

   https://doi.org/10.1109/CDC42340.2020.9304015  

   Gohari, Parham ; Hale, Matthew ; Topcu, Ufuk ( December 2020 , Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC))

   null (Ed.)

   In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation’s investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protects the privacy of the transition probabilities in a Markov decision process. We use differential privacy as the mathematical definition of privacy. The algorithm first perturbs the transition probabilities using a mechanism that provides differential privacy. Then, based on the privatized transition probabilities, we synthesize a policy using dynamic programming. Our main contribution is to bound the "cost of privacy," i.e., the difference between the expected total rewards with privacy and the expected total rewards without privacy. We also show that computing the cost of privacy has time complexity that is polynomial in the parameters of the problem. Moreover, we establish that the cost of privacy increases with the strength of differential privacy protections, and we quantify this increase. Finally, numerical experiments on two example environments validate the established relationship between the cost of privacy and the strength of data privacy protections. 

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4. Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms

   J. Seeman, M. Reimherr ( January 2021 , Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021)

   Marc'Aurelio Ranzato, Alina Beygelzimer (Ed.)

   Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carlo (MCMC) are used, the end result incurs costs to both privacy and accuracy. Existing work has examined these effects asymptotically, but implementable finite sample results are needed in practice so that users can specify privacy budgets in advance and implement samplers with exact privacy guarantees. In this paper, we use tools from ergodic theory and perfect simulation to design exact finite runtime sampling algorithms for the exponential mechanism by introducing an intermediate modified target distribution using artificial atoms. We propose an additional modification of this sampling algorithm that maintains its ǫ-DP guarantee and has improved runtime at the cost of some utility. We then compare these methods in scenarios where we can explicitly calculate a δ cost (as in (ǫ, δ)-DP) incurred when using standard MCMC techniques. Much as there is a well known trade-off between privacy and utility, we demonstrate that there is also a trade-off between privacy guarantees and runtime. 

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5. iTPM: Exploring PUF-based Keyless TPM for Security-by-Design of Smart Electronics

   https://doi.org/10.1109/ISVLSI59464.2023.10238586  

   Bathalapalli, Venkata K. ; Mohanty, Saraju P. ; Kougianos, Elias ; Iyer, Vasanth ; Rout, Bibhudutta ( June 2023 , 2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   Fernanda Kastensmidt Ricardo Reis Aida Todri-Sanial Hai (Ed.)

   The scope of Smart electronics and its increasing market worldwide has made cybersecurity an important challenge. The Security-by-Design (SbD) principle, an emerging cybersecurity area, focuses on building security/privacy-enabled primitives at the design stage of an electronic system. This paper proposes a novel Physical Unclonable Function (PUF) based Trusted Platform Module (TPM) for SbD primitive. The proposed SbD primitive works by performing secure verification of the PUF key using TPM’s Encryption and Decryption engine. The securely verified PUF Key is then bound to TPM using Platform Configuration Registers (PCR). PCRs in TPM facilitate a secure boot process and effective access control to TPM’s NonVolatile memory through an enhanced authorization policy. By binding PUF with PCR in TPM, a novel PUF-based access control policy can be defined, bringing in a new security ecosystem for the emerging Internet-of-Everything era. The proposed SbD approach has been experimentally validated by successfully integrating various PUF topologies with Hardware TPM. 

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