This is my presentation slides at the ONR "Science of Autonomy" program review, at August 2023. I discussed our progress on establishing the notion of guaranteed-privacy- as a robust counterpart of differential privacy- by leveraging set-valued perturbations instead of stochastic noise signals, and functional perturbations instead of perturbing data. Then, I showed how robustness against distributions and algorithms, hard accuracy bounds and nonconvexity can be addressed by this new notion of guaranteed privacy. However, I described our thoughts and plans on designing guaranteed privacy-preserving control strategies, attack mitigation techniques, and robust dynamic games in multi-agent systems.

1. Guaranteed Privacy-Preserving Mechanisms
in Multi-Agent Systems
Mohammad Khajenejad, Sonia Martinez (PI)
Department of Mechanical and Aerospace Engineering
University of California, San Diego, USA
ONR Science of Autonomy Program Review
Virtual Presentation
August 11, 2023

2. Robust, Safe, Resilient, Private, and Distributed Autonomy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 2 / 28

3. Robust, Safe, Resilient, Private, and Distributed Autonomy
uncertainties =⇒ robustness
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 2 / 28

4. Robust, Safe, Resilient, Private, and Distributed Autonomy
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 2 / 28

5. Robust, Safe, Resilient, Private, and Distributed Autonomy
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
attacks =⇒ resiliency
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 2 / 28

6. Robust, Safe, Resilient, Private, and Distributed Autonomy
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
attacks =⇒ resiliency
data protection =⇒ privacy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 2 / 28

7. Robust, Safe, Resilient, Private, and Distributed Autonomy
heterogeneous, local =⇒ networked cps
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
attacks =⇒ resiliency
data protection =⇒ privacy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 2 / 28

8. Assured, Strategic, & Hybrid Autonomy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 3 / 28

9. Assured, Strategic, & Hybrid Autonomy
resiliency privacy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 3 / 28

10. Assured, Strategic, & Hybrid Autonomy
resiliency privacy
strategic decision-making
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 3 / 28

11. Assured, Strategic, & Hybrid Autonomy
resiliency privacy
strategic decision-making hybrid & unknown CPS
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 3 / 28

12. Private, Secure & Strategic Decision-Making
What notion of privacy is consistent with secure and strategic
decision-making?
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 4 / 28

13. Private, Secure & Strategic Decision-Making
What notion of privacy is consistent with secure and strategic
decision-making?
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 4 / 28

14. Privacy
to protect valuable data, identity, control strategy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 5 / 28

15. Privacy-Preserving Mechanism
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 6 / 28

16. Differential Privacy
Dwork et al. (2006), Huang et al. (2015), Hale et al. (2015), Wang et al.
(2017), Han et al. (2021), Ding et al. (2022), Ye et al. (2022), ...
Pr[M(x) ∈ S] ≤ e
Pr[M(y) ∈ S] + δ
rondom perturbation
performance loss
stochastic accuracy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 7 / 28

17. Differential Privacy
Dwork et al. (2006), Huang et al. (2015), Hale et al. (2015), Wang et al.
(2017), Han et al. (2021), Ding et al. (2022), Ye et al. (2022), ...
Pr[M(x) ∈ S] ≤ e
Pr[M(y) ∈ S] + δ
rondom perturbation
performance loss
stochastic accuracy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 7 / 28

18. Encryption-Based Privacy
Lu et al. (2018), Darup et al. (2021), Fioravanti et al. (2022), Wang et al.
(2022), An et al. (2022), ...
security, confidentiality,
integrity
computational overhead
key loss risks
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 8 / 28

19. Encryption-Based Privacy
Lu et al. (2018), Darup et al. (2021), Fioravanti et al. (2022), Wang et al.
(2022), An et al. (2022), ...
security, confidentiality,
integrity
computational overhead
key loss risks
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 8 / 28

20. Stochastic Functional Perturbation
Chaudhuri et al. (2011), Zhang et al. (2012), Hall et al. (2013), Cortez et al.
(2016), Nozari et al. (2018), Li et al. (2020),
stochastic guarantee
limited functional space
convexity required
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 9 / 28

21. Stochastic Functional Perturbation
Chaudhuri et al. (2011), Zhang et al. (2012), Hall et al. (2013), Cortez et al.
(2016), Nozari et al. (2018), Li et al. (2020),
stochastic guarantee
limited functional space
convexity required
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 9 / 28

22. Towards Guaranteed Privacy
We require ...
hard accuracy bounds
robustness to distributions
to address nonconvexity
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23. Idea
interval methods to design optimal perturbations
robust optimization to quantify error bounds
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 11 / 28

24. Guaranteed Private Distributed Optimization
(a) true objective, (b) perturbed objective
original optimization
min
x∈X0
f (x) ,
PN
i=1fi (x)
functionally perturbed optimization
min
x∈X0
g(x),
PN
i=1fi (x)+˜
fi (x)
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 12 / 28

25. Guaranteed Private Distributed Optimization
distributed nonconvex optimization
min
x∈X0
f (x) ,
PN
i=1fi (x)
(a) true objective (b) perturbed objective
to implement a range perturbation of functions to robustify
the optimization problem in a controlled manner by an gap
diam(M(F0, X)∩I)≤ e
kfi0
−f 0
i0
kV
diam(M(F, X))
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 13 / 28

26. Guaranteed Private Distributed Optimization
distributed nonconvex optimization
min
x∈X0
f (x) ,
PN
i=1fi (x)
(a) true objective (b) perturbed objective
to implement a range perturbation of functions to robustify
the optimization problem in a controlled manner by an gap
diam(M(F0, X)∩I)≤ e
kfi0
−f 0
i0
kV
diam(M(F, X))
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 13 / 28

27. Designing The Perturbations
perturbed function
z }| {
gi
(x) =
f i (x), true function
z }| {
hi
(x)
| {z }
JSS mapping
+ mi
x
|{z}
linear remainder
+
perturbation
z}|{
m̃i
x
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-1
-0.5
0
0.5
1
1.5
2
2.5
3
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
to minimize the difference between the linear remainder and
the perturbation function
min
{ξ∈R2n+1,p1,p2∈R3n}
0
2n 1
ξ
s.t. Λξ ≤ l, p
1 d ≤ 0, p
2 d ≤ 0,
Γ
p1 = ξ, −Γ
p2 = ξ, p1 ≥ 03n, p2 ≥ 03n,
m̃∗
= (ξ∗
)
In −In 0
n
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 14 / 28

28. Designing The Perturbations
perturbed function
z }| {
gi
(x) =
f i (x), true function
z }| {
hi
(x)
| {z }
JSS mapping
+ mi
x
|{z}
linear remainder
+
perturbation
z}|{
m̃i
x
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-1
-0.5
0
0.5
1
1.5
2
2.5
3
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
to minimize the difference between the linear remainder and
the perturbation function
min
{ξ∈R2n+1,p1,p2∈R3n}
0
2n 1
ξ
s.t. Λξ ≤ l, p
1 d ≤ 0, p
2 d ≤ 0,
Γ
p1 = ξ, −Γ
p2 = ξ, p1 ≥ 03n, p2 ≥ 03n,
m̃∗
= (ξ∗
)
In −In 0
n
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 14 / 28

29. Designing The Perturbations
perturbed function
z }| {
gi
(x) =
f i (x), true function
z }| {
hi
(x)
| {z }
JSS mapping
+ mi
x
|{z}
linear remainder
+
perturbation
z}|{
m̃i
x
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-1
-0.5
0
0.5
1
1.5
2
2.5
3
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
to minimize the difference between the linear remainder and
the perturbation function
min
{ξ∈R2n+1,p1,p2∈R3n}
0
2n 1
ξ
s.t. Λξ ≤ l, p
1 d ≤ 0, p
2 d ≤ 0,
Γ
p1 = ξ, −Γ
p2 = ξ, p1 ≥ 03n, p2 ≥ 03n,
m̃∗
= (ξ∗
)
In −In 0
n
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 14 / 28

30. Error Bounds
distributed nonconvex optimization
min
x∈X0
f (x) ,
PN
i=1fi (x)
functional perturbation
g(x),
PN
i=1fi (x)+
unknown, deterministic
z}|{
m̃i x
(a) true objective (b) perturbed objective
Theorem
= maxi∈{1,...,N} i -guaranteed privacy is satisfied, with i = β(fi , m̃i , X0, δi )
and arbitrary m̃i such that m̃i ∆ ≤ δ∗
i
The (worst-case) accuracy error satisfies: maxx∗∈Xf ,x̃∗∈Xgkx∗−x̃∗k∞≤UB
UB = max
{y∈X0,z∈X0,θ∈R≥0}
θ
s.t − θ1n≤y−z≤θ1n, m̃i (y−z)≤0, 1≤i≤N
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 15 / 28

31. Error Bounds
distributed nonconvex optimization
min
x∈X0
f (x) ,
PN
i=1fi (x)
functional perturbation
g(x),
PN
i=1fi (x)+
unknown, deterministic
z}|{
m̃i x
(a) true objective (b) perturbed objective
Theorem
= maxi∈{1,...,N} i -guaranteed privacy is satisfied, with i = β(fi , m̃i , X0, δi )
and arbitrary m̃i such that m̃i ∆ ≤ δ∗
i
The (worst-case) accuracy error satisfies: maxx∗∈Xf ,x̃∗∈Xgkx∗−x̃∗k∞≤UB
UB = max
{y∈X0,z∈X0,θ∈R≥0}
θ
s.t − θ1n≤y−z≤θ1n, m̃i (y−z)≤0, 1≤i≤N
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 15 / 28

32. Accuracy vs Privacy: Guaranteed Upper Bounds
Left: theoretical accuracy error upper bound, as well as true accuracy error obtained
by applying several nonconvex distributed optimization algorithms
Right: comparison of the guaranteed privacy errors and upper bound with the one
from a differential private distributed optimization algorithm
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 16 / 28

33. Guaranteed Privacy-Preserving Control (ongoing)
privacy-preserving cruise control
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 17 / 28

34. Guaranteed Privacy-Preserving Control (ongoing)
privacy-preserving cruise control
guaranteed privacy-preserving distributed MPC
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 17 / 28

35. Guaranteed Privacy-Preserving Dynamic Control
individual and best (thick solid line) trajectories under guaranteed privacy-preserving
dynamic control with optimal measurement aggregation
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 18 / 28

36. Future Work 1
Privacy Meets Resiliency
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 19 / 28

37. Multi-Agent CPS Under Attack
Target system, x ∈ Rn
x+
= f (x, w, d)
w ∈ [w, w], d ∈ Rp
d is unknown and arbitrary
Sensor network, i = 1, . . . , N
yi
= hi
(x, vi
, d), vi
∈ [vi
, vi
]
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 20 / 28

38. Scalable Distributed Resiliency
Network update: min/max consensus
xi,t
k = max
j∈Ni
xj,t−1
k xi,t
k = min
j∈Ni
xj,t−1
k
xi
k = xi,tx
k xi
k = xi,tx
k
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 21 / 28

39. Future Work: 1. Privacy Meets Resiliency
adversary can both steal valuable data and inject attack
to simultaneously protect data and mitigate attacks
level of tolerance ⇒ privacy-preserving resilient control
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 22 / 28

40. Future Work: 1. Privacy Meets Resiliency
adversary can both steal valuable data and inject attack
to simultaneously protect data and mitigate attacks
level of tolerance ⇒ privacy-preserving resilient control
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 22 / 28

41. Future Work: 1. Privacy Meets Resiliency
adversary can both steal valuable data and inject attack
to simultaneously protect data and mitigate attacks
level of tolerance ⇒ privacy-preserving resilient control
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 22 / 28

42. Future Work 2
Strategic Heterogeneous
Adversaries
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 23 / 28

43. Future Work: 2. Strategic Heterogeneous Adversaries
strategic agents
heterogeneous beliefs/types
bounded rationality
local communication
Can we preserve privacy against strategic adversaries?
guaranteed privacy-preserving dynamic games
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 24 / 28

44. Future Work: 2. Strategic Heterogeneous Adversaries
strategic agents
heterogeneous beliefs/types
bounded rationality
local communication
Can we preserve privacy against strategic adversaries?
guaranteed privacy-preserving dynamic games
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 24 / 28

45. Future Work: 2. Strategic Heterogeneous Adversaries
strategic agents
heterogeneous beliefs/types
bounded rationality
local communication
Can we preserve privacy against strategic adversaries?
guaranteed privacy-preserving dynamic games
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 24 / 28

46. Data-Driven Set-Membership Learning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 25 / 28

47. Future Work: 2. Strategic Heterogeneous Adversaries
set-membership learning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 26 / 28

48. Future Work: 2. Strategic Heterogeneous Adversaries
set-membership learning
+
robust dynamic games
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 26 / 28

49. Future Work: 2. Strategic Heterogeneous Adversaries
set-membership learning
+
robust dynamic games
+
privacy-preserving control
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 26 / 28

50. Future Work: 2. Strategic Heterogeneous Adversaries
set-membership learning
+
robust dynamic games
+
privacy-preserving control
guaranteed privacy-preserving dynamic games
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 26 / 28

51. Takeaway
perturbation





random → set-based
data → functional
⇒ guaranteed privacy
hard bounds, robustness, nonconvexity
future: guaranteed private control, attack mitigation, game
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 27 / 28

52. Takeaway
perturbation





random → set-based
data → functional
⇒ guaranteed privacy
hard bounds, robustness, nonconvexity
future: guaranteed private control, attack mitigation, game
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 27 / 28

53. Takeaway
perturbation





random → set-based
data → functional
⇒ guaranteed privacy
hard bounds, robustness, nonconvexity
future: guaranteed private control, attack mitigation, game
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 27 / 28

54. Thank you!
Questions?
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 28 / 28

55. Back-Up Slides
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 29 / 28

56. Data Attack Resiliency
Can we simultaneously obtain guaranteed estimates of states and
unknown inputs (adversarial signals) and possibly mitigate their
effect?
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 30 / 28

57. Vision Overview
resiliency
+
privacy
strategic decision making unknown CPS
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 31 / 28

58. Future Vision 3. Towards Hybrid Unknown CPS
hybrid reachability and invariance properties
NSF-CPS, NASA-NSPIRES early career award,
Amazon-Automated Reasoning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 32 / 28

59. Future Vision 3. Towards Hybrid Unknown CPS
hybrid reachability and invariance properties
nonconvex optimization
NSF-CPS, NASA-NSPIRES early career award,
Amazon-Automated Reasoning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 32 / 28

60. Future Vision 3. Towards Hybrid Unknown CPS
hybrid reachability and invariance properties
nonconvex optimization
unknown CPS: set-membership learning
meets model-based approaches
NSF-CPS, NASA-NSPIRES early career award,
Amazon-Automated Reasoning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 32 / 28

61. Future Vision 3. Towards Hybrid Unknown CPS
hybrid reachability and invariance properties
nonconvex optimization
unknown CPS: set-membership learning
meets model-based approaches
aleatoric+epistemic uncertainties:
random sets
NSF-CPS, NASA-NSPIRES early career award,
Amazon-Automated Reasoning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 32 / 28

62. Future Vision 3. Towards Hybrid Unknown CPS
hybrid reachability and invariance properties
nonconvex optimization
unknown CPS: set-membership learning
meets model-based approaches
aleatoric+epistemic uncertainties:
random sets
NSF-CPS, NASA-NSPIRES early career award,
Amazon-Automated Reasoning
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 32 / 28

63. System Properties ⇒ Safe Secure Autonomy
Research Question
Can we leverage dynamic systems’ properties to obtain robust, resilient,
distributed private autonomy?
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 33 / 28

64. Takeaway















mixed-monotonicity
strong detectability
collective positive detectability
X
=
⇒















robust reachability
attack mitigation
distributed resiliency
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 34 / 28

65. Possible Collaborations
Prof. Javad Velni
I set-membership learning-based model predictive control
I attack resilient safe control of unknown nonlinear systems
Prof. Beshah Ayalew
I robust data-driven CBFs for RL-based safe ACC
I game-theoretic distributed safe optimal control of traffic networks
Prof. Chris Paredis
I abstraction-based formal verification for vehicular control systems
I resilient exploration of extreme and unstructured environments for UGVs
Prof. Ge Lv
I reachability analysis-based control of exoskeletons
I hybrid invariance properties of contact dynamics in bipedal robots
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 35 / 28

66. Possible Collaborations
Prof. Javad Velni
I set-membership learning-based model predictive control
I attack resilient safe control of unknown nonlinear systems
Prof. Beshah Ayalew
I robust data-driven CBFs for RL-based safe ACC
I game-theoretic distributed safe optimal control of traffic networks
Prof. Chris Paredis
I abstraction-based formal verification for vehicular control systems
I resilient exploration of extreme and unstructured environments for UGVs
Prof. Ge Lv
I reachability analysis-based control of exoskeletons
I hybrid invariance properties of contact dynamics in bipedal robots
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 35 / 28

67. Possible Collaborations
Prof. Javad Velni
I set-membership learning-based model predictive control
I attack resilient safe control of unknown nonlinear systems
Prof. Beshah Ayalew
I robust data-driven CBFs for RL-based safe ACC
I game-theoretic distributed safe optimal control of traffic networks
Prof. Chris Paredis
I abstraction-based formal verification for vehicular control systems
I resilient exploration of extreme and unstructured environments for UGVs
Prof. Ge Lv
I reachability analysis-based control of exoskeletons
I hybrid invariance properties of contact dynamics in bipedal robots
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 35 / 28

68. Possible Collaborations
Prof. Javad Velni
I set-membership learning-based model predictive control
I attack resilient safe control of unknown nonlinear systems
Prof. Beshah Ayalew
I robust data-driven CBFs for RL-based safe ACC
I game-theoretic distributed safe optimal control of traffic networks
Prof. Chris Paredis
I abstraction-based formal verification for vehicular control systems
I resilient exploration of extreme and unstructured environments for UGVs
Prof. Ge Lv
I reachability analysis-based control of exoskeletons
I hybrid invariance properties of contact dynamics in bipedal robots
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 35 / 28

69. Future Vision: 2. Heterogeneous and Strategic Agents
heterogeneous beliefs/types
bounded rationality
strategic vs. best worst-case
local communication
robust dynamic/differential
networked games
ARL, DARPA-ARC, AFOSR-YIP
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 36 / 28

70. Teaching Interests
undergraduate:
I control integration of multi-domain dynamic systems (ME 4030)
I mechatronics system design (ME 4710)
I nonlinear dynamics chaos (ME 4500)
graduate:
I advanced dynamics (ME 8430)
I modern control engineering (ME 8200)
I applied optimal control (ME 8220)
to create:
I resilient private decision-making in networked CPS
F control theory+optimization+decision sciences
F fundamentals of resilient estimation and control
F distributed privacy-preserving mechanisms
F safety under various sources of uncertainty
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 37 / 28

71. Teaching Interests
undergraduate:
I control integration of multi-domain dynamic systems (ME 4030)
I mechatronics system design (ME 4710)
I nonlinear dynamics chaos (ME 4500)
graduate:
I advanced dynamics (ME 8430)
I modern control engineering (ME 8200)
I applied optimal control (ME 8220)
to create:
I resilient private decision-making in networked CPS
F control theory+optimization+decision sciences
F fundamentals of resilient estimation and control
F distributed privacy-preserving mechanisms
F safety under various sources of uncertainty
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 37 / 28

72. Teaching Interests
undergraduate:
I control integration of multi-domain dynamic systems (ME 4030)
I mechatronics system design (ME 4710)
I nonlinear dynamics chaos (ME 4500)
graduate:
I advanced dynamics (ME 8430)
I modern control engineering (ME 8200)
I applied optimal control (ME 8220)
to create:
I resilient private decision-making in networked CPS
F control theory+optimization+decision sciences
F fundamentals of resilient estimation and control
F distributed privacy-preserving mechanisms
F safety under various sources of uncertainty
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 37 / 28

73. Robust, Safe, Resilient, Private and Distributed Autonomy
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 38 / 28

74. Robust, Safe, Resilient, Private and Distributed Autonomy
uncertainties =⇒ robustness
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 38 / 28

75. Robust, Safe, Resilient, Private and Distributed Autonomy
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 38 / 28

76. Robust, Safe, Resilient, Private and Distributed Autonomy
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
attacks =⇒ resiliency
M. Khajenejad (UCSD) Guaranteed Privacy August 11, 2023 38 / 28

77. Robust, Safe, Resilient, Private and Distributed Autonomy
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
attacks =⇒ resiliency
data protection =⇒ privacy
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78. Robust, Safe, Resilient, Private and Distributed Autonomy
heterogeneous, local =⇒ networked cps
uncertainties =⇒ robustness
unsafe regions =⇒ safety critical
attacks =⇒ resiliency
data protection =⇒ privacy
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79. System Properties ⇒ Safe Secure Autonomy
Research Question
Can we leverage dynamic systems’ properties to obtain robust, resilient,
distributed private autonomy?
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80. System Properties ⇒ Safe Secure Autonomy
Research Question
Can we leverage dynamic systems’ properties to obtain robust, resilient,
distributed private autonomy?
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81. Past Research: Resilient Set-Valued Estimation Control
state estimation and Input reconstruction are important for
fault detection, attack mitigation, intent estimation etc.
Constrained Nonlinear System
G :
(
x+
t = f (xt, wt, dt),
yt = h(xt, vt, dt),
Motivating question
Can we simultaneously estimate “ sets” of states and unknown
inputs and possibly mitigate the effect of attacks?
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82. Past Research Overview: Set-Valued Methods
Distribution-free
uncertainty sets
Robust reachability analysis
State estimation and attack mitigation
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83. Now?
Towards Networked CPS
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84. Current Research: Scalable Distributed Resiliency
Network update: min/max consensus
xi,t
k = max
j∈Ni
xj,t−1
k xi,t
k = min
j∈Ni
xj,t−1
k
xi
k = xi,tx
k xi
k = xi,tx
k
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85. Perspective
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86. Funding Opportuinities
NSF CAREER Award ($500k / 5 years)
I All assistant profs, up to 3 attempts
Army Research Lab (ARL)
DoD Young Investigator Programs ($500k / 3 years)
I AFOSR, ONR, ARO
I Assistant profs within 5 years of PhD
DoE Early Career Award ($750k / 5 years)
I Assistant profs within 10 years of PhD
DARPA Young Faculty Award ($300k / 2 years)
I Assistant profs within 10 years of PhD
NASA Early Career Faculty Award ($600k / 3 years)
Industry grants (Google, Amazon, Ford, Toyota, etc.)
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87. Future Vision: 3. Guaranteed Privacy-Preserving Mechanism Design
Existing notions of privacy: either sacrifice accuracy or incur large
computation or communication overhead
Need for hard accuracy bounds
Towards guaranteed private estimation, control and verification by
leveraging unknown but deterministic functional perturbations
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88. Future Vision: 3. Guaranteed Privacy-Preserving Mechanism Design
Existing notions of privacy: either sacrifice accuracy or incur large
computation or communication overhead
Need for hard accuracy bounds
Towards guaranteed private estimation, control and verification by
leveraging unknown but deterministic functional perturbations
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89. Future Vision: 3. Guaranteed Privacy-Preserving Mechanism Design
Existing notions of privacy: either sacrifice accuracy or incur large
computation or communication overhead
Need for hard accuracy bounds
Towards guaranteed private estimation, control and verification by
leveraging unknown but deterministic functional perturbations
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90. Future Vision: 4. Uncertain and Hybrid Networked CPS
Hybrid reachability and invariance properties Hidden mode CPS: MM framework
Unknown CPS: set-membership learning
meets model-based approaches
Aleatoric+epistemic uncertainties:
random sets
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91. Thank you! Questions?
Taha, Fatemeh, Marsa Sze Zheng Yong Sonia Martinez
My labmates
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